{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Multiclass Support Vector Machine exercise\n",
    "\n",
    "*Complete and hand in this completed worksheet (including its outputs and any supporting code outside of the worksheet) with your assignment submission. For more details see the [assignments page](http://vision.stanford.edu/teaching/cs231n/assignments.html) on the course website.*\n",
    "\n",
    "In this exercise you will:\n",
    "    \n",
    "- implement a fully-vectorized **loss function** for the SVM\n",
    "- implement the fully-vectorized expression for its **analytic gradient**\n",
    "- **check your implementation** using numerical gradient\n",
    "- use a validation set to **tune the learning rate and regularization** strength\n",
    "- **optimize** the loss function with **SGD**\n",
    "- **visualize** the final learned weights\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Run some setup code for this notebook.\n",
    "\n",
    "import random\n",
    "import numpy as np\n",
    "from cs231n.data_utils import load_CIFAR10\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from __future__ import print_function\n",
    "\n",
    "# This is a bit of magic to make matplotlib figures appear inline in the\n",
    "# notebook rather than in a new window.\n",
    "%matplotlib inline\n",
    "plt.rcParams['figure.figsize'] = (10.0, 8.0) # set default size of plots\n",
    "plt.rcParams['image.interpolation'] = 'nearest'\n",
    "plt.rcParams['image.cmap'] = 'gray'\n",
    "\n",
    "# Some more magic so that the notebook will reload external python modules;\n",
    "# see http://stackoverflow.com/questions/1907993/autoreload-of-modules-in-ipython\n",
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## CIFAR-10 Data Loading and Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training data shape:  (50000, 32, 32, 3)\n",
      "Training labels shape:  (50000,)\n",
      "Test data shape:  (10000, 32, 32, 3)\n",
      "Test labels shape:  (10000,)\n"
     ]
    }
   ],
   "source": [
    "# Load the raw CIFAR-10 data.\n",
    "cifar10_dir = 'cs231n/datasets/cifar-10-batches-py'\n",
    "X_train, y_train, X_test, y_test = load_CIFAR10(cifar10_dir)\n",
    "\n",
    "# As a sanity check, we print out the size of the training and test data.\n",
    "print('Training data shape: ', X_train.shape)\n",
    "print('Training labels shape: ', y_train.shape)\n",
    "print('Test data shape: ', X_test.shape)\n",
    "print('Test labels shape: ', y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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Q8zPUag101KZSq6G1JY4MUWzpxDFhqJmfX8QgKEdRrVYZKPv4vk+z2cU1EVnfksv45D2wxRyBKNrLywRDBTrdmFxOkQ16y4WVMVn5DCsLKMAqOSDJ4isvMjRYLBaMAwjGFcQkC/U5JXAV3/aCIxFgEQNYlTwjmTGIWEQMCl70LKtNwkvaYgS0SYQ4zkr952WWGAt2pY295wy8CkJciWCiCFwXjEHSlW81Vjo0WdsuPecupk0n47m2cF5hhlcTJXGIOh5nDyyTCzbRbIT09blsKCvK+SH6BsaZbzVYimoooFNbIu8GWB1irEEpJ9UUEyYwcSrErU26QClQ+kUMcjGM9+WxYQcyXqL5ChhrcbTGx1Iav476zAGi+iJKCcYkwtRzHVpzM7S6EZJx8cU51//nJ9z5ul2396GSK0dHrPCD53nksgFRGKJNTKxjBIODIBaUOCjJ0KzHxK029WqHa667kdvvuoeHvvhxdBTT7XbQWgMJ3UqpdMJdGsYYHMdZtaC8eHIrEewqHkvEiUGwWKMZHh7k3e98Gzdeu42+nI8bFMgVSrz7nW/l6QNP89GP/10y5dfYDZ6ePMZie4a27XLbjTciRMRExHGTheVJjFiGRgvUFwvorIPnxlSqi3Q7JQrFDL7yGOobwpUCZ49PMzzUTyHXhw0MzVad5UaNOGqh/N7jIjjEkWFhfpnQCtYIjUaLIJujUMgThjGuK2hrMMYSZDJorYnjGGPA9QJyeQffF8QRyv0lonaE1pZm27JYmeeGG3bS35/vTceqBdMahRUwku5UAdHnVb/zw5MsUNYqjE1/l0RQij2vNCilUj4B5fTuj2JgiVRyv9apTdoKlmTeOZ5C1AotiRJgJD0bxaYau5MsPg4rZ6ac5ydjV3jj8qwFV1WIV6sV5o4dozq3xPY7bqW/2PfNAhxo1ZvUF+vEUcTY1g0gFksyqVdr3iKSmFBS7dCSrGAv0pYugW8HTbxq62QLwta9WZ742wO06xk6A8PMamFxeor+oTlOz04SFD027d3K6Kb9HH/uQbbfUsboFkYLcWQJu9CoCTpOBJTjaNzAojIO4kQ4qrfQypsO3VaMGJ/AFbCgrAVdo39kjLY4OMMOfQOLtJZOoEyMl81jK4eoHLX4QzuxWQ/druJJCSQRaJIy9YqG4vt+Tzo8T6F1omHv2LmF/oFhdl9zPX3lMgPlPh783Cc5fuQIS9U6yvGxxuMbX3+OsfF5cvkyt9xyJ7fddgvTp09y6MiznDp7ik5LI0pwHIcwDBEjay7gNlUfLma6U0oRm4TXzgl6a1FoHIHYxLzzbW9l28YxJo+/wO/96n/gh37kx1larvPAV77KD/zYj/HpL3yZarVJvEZayVznBOVyhnyUIZstYTMxzagLhQw7du+iWM7TWdZIx8dxHGanjlMLT1Gv9TE2vI18MMDm8TKPPPoMgeehjcfp0xWWlqq0uzVued0uzk6eYGZqricdrhJGR8fw8dAmor+viDIOlUoNx3OJLFy773ry/YMcef4QlblJdm8b58jJKdphTN4DoyzdyGW03E8m4xDlYzDC/GINT+VpNGO2bOt9KoHWmnYcYtJpqy7YVPninNOqz8Mkpo70vC2RdCxNqjyu8IIIWsc41lmTPzJZDzc152BX7wAEpYTAF3wnqTKODZERYq0Srd2kB1fBitYCqGS3bJP9XCRgzeWbe6+qEO/r6yezcxcn9VEefvhhNoyMsXXrFkqlPpRyQEHcjakt1ZifnkMsDA4NIK5CeS7WZUXtobZQxfVc8n2rjllYWeiu2OJ+daFiwYoBr8PWN4yRccvcvOdWOlMRD3zyIc4cPYVtdogWHOYaJzDVNp6TwWjwcTDaEGuNsYJVdpUmIAQZwfVBKUG5ay5poDU2inGVpMJXsHEL3aoQZ0eJjKLdsWQyOXzHpxtaCqUhgmKBoOCzWG+QqdVxJ+w3KRDGJgzveMHFq1+hwiabKM9zKZdLbJiY4Dve+lYQh1a7zb6bbyfGg8lJGo0ujWYHJzIMjW5gYuM2rt27jw3jY5w6doC5yWnCVgdtFBiI4xjXdRM61tC0VrQ1JLFVKsdJtDcFKIvVcs7WCibdqCu0tXi+x9BAHyNDIzhRk0ymwOjoKEdPHOL5F57gzPEXuGbnbh554llYw4yxY+cuCp5LQAk/6Mf4wqCXIZ/NoTttCsUS1bBOqVQm6/ksi+HM6ZMMD2+lEES4xSLHT02yXFOMjWRYqtSYmpri+PFjdKMGd969FxGh1Wj0pMPJ9RFZaMSAgYJYPN9AYBHf5do9+3jvP/mnBPlBju87wckjL3D65JMQBDTqIXNzdeZrNcRJxM22zWPUWw1cXLKFPEZDs2U4cLD3uW1aa2TFlJdY2V7EaiuaNJxfeB1HiHScKDLKR7QBpc6ZzFYG3BqNSc0aoi92VtZ5eF4q+8/JmhUN36KUxVWgUsXTcRPBnRrrU+EtGGPO2eRXlO7ELGMRK9iX8JqPq25O8Ysl9rxuP4WZGSZPn+XTn/oUN9/0Onbu2IWNNZ1WSLva5siho7hKUe4rMTA8hI3BVS7iKxCYOnmWIJ9he1/yOrrE/GLP2bhefWPJ2vBwUAa0AacIVrWYahxj754b+cCPfCfPfOMpjj0/zbEDZ2k1F4maLrnxRIhiw3NC21WQ8RWxSGq/tXiegxcI1gprHf2vUjuu1TGOJOYUqwQiQ7NyivKG3Wi/i1mcp73QxhaKtDo1Cn2jKGOwrWUGS4M0ogwmaqPcgPMLqcUagygX3Wn1pGNl4vX3l9myZQtbNm+mubzIybPTdGPLtj376Gih2rUsN88gbsDGjZvZvn07w0PD6LDB0099ncnJYzTqTUTUObt2HMcvclL2QqKtrTiXnGQLn7gZkl2G+Ml3zCpTlYPF4nmKfNZjw/gEWyfGoBszPjLGyHCZXFZx6MDT3H3bbTz57At0Os2edJSKfcSdDpGxODrGkRyZTB9uwdChTqEwgDYu1otRnkMu52F1QLOmmTUVTh6t0zHCvutu4PTpE3S7LQ4fO0oYRYyOjXHy7Cm0NmT93otrKIpOGFNtdRAdky8FDJZLDI3naYURN956BxNbdrBUbdI3OMo1uT4yfS42yNPqHuX02UU6kYcYOHV6HmLLxrEy+UxAR4eEsUOzGeF6vRk1MUeQ7HwMmBXl9Vz5+Q8m/dKpV2m1Ghix9I1uwkuG8MXPFAEFBpMIWN175+r64BqH86631MFxbtdp0p2+QjmCo1TCO6lsSsyRqTVBpaYUS9oYOaeV229Hc8oKrLVMbNhAu9nh8b/6aw4dPskb77mX8dIgNk5IHxkbpbq8zNPPPceOnTsYKPeT62bJlvO4WZ92q8Xg2NCLnpvYzF6NFl0ZrBWMBUEl2y8VMV05Q6cdsv/a/fzAT72f5kLEo197mla7w+ZrdvAHf/mXqAis56AlRkTIuA6SEUIP4lhwXQh8B8dJGGYt25paURSswcGAXeE4obN8hjiO8CXC5LJEuWHEz1IcLpLp68f3A0Qp2o15YqNRFSEzvAORKLERW0HExVmeZfrUN+C2u3r0R2KzzuXzKKXoNKo88A9/x+RchQ1btnPNnt3s3XcDR4+f5OTJ0/iuQymXobY4R2d5kgNPzNNq1xDH0jEGcX2IEuHtuu6LzHBrj02y7XUcQUmq/SWhAyhZ0eLsOZ9NMuUM1li67TY2jhBH41nN0uwsEsOHPvB9dDqGXTftZeOGAZ470NuMgYXphTkazTY7fCGu+/goAiyunyPI58hh6ZoWzUqFVqNOKT9BpRKSD4SFhTlGJzbRbtUJMg7LtQqnp6Yo9/Wz55q9HDv6DfqzeZTp3R/ZrE+xr8T81Azdahvd1djIJTQxOHDD9Tfjuj6es0S3W2W52mLrtmuZGBmjValz+uwk4VKdjBL8KKCzKHiDZQIvg5KI2KuwvFijVCr17o8Vs6lJ7MaJK5FzjrNz8Q2rTCr1xQW69SpBfz/aaLDJcrt6nEWpRPs1nNvR9oKvUp+Iu2IGWdGiE1OgIw7q3NwGhRC4YIxFiyT8Yy1GeSidCnBRRBa0taBXHPqXh1fn/XdKUas3+Iu//BiPPP4knuvTbHR54+13Y7TB911yxRzlgQGmZ6Y4feo07eUG+XyWvtFBxrZNEHa7+JkXv4VsRYi/VhKYojBGTIwRheN64ILxLQ1d4cDJg+zYuJMtEwO87wP3kM/l8bPDNMMWTx/8KvRlOLUwiSMKwZLLOqhIEymL4yhcJ2GERFFcwwYsic1aCWA0SRyWYEQgbOLpGtq0cHMDZPsMynETs4dS4GbA1PGNIWdqNKanEKtxSiNYN4urLJUjj+Gdfhib6+24spaEdiVU5ueozk5SXZinHRlKe7bSqc8wv7jMYP8gExs2s7gwz8L8aTaNZzAmYursURzXpdZo0u3E+L6P5yXhY3Ecp9pZsuNYC+edmomzMmlzcr8iRnBYLcQdIdHYrabZqIGOmTxznPnpM0yMb2Kkf4TrbriRqek5iqMD7Ng2wYGDB3vS4IiPsYIKLN24RaNew9GK8ZERMqU8XRPj5wsEjqVSX2RhsUHYCejvGyKXK1IsdVmYm+Xs9BT3vPlunj34HCdOneGanQEmDin4DssL8zTrYU86AmvRnTYjfXkiX1MsBIStNt04Jj/QR1+piNJd+jIeeddisg5xrcn45g28473vYfLUAjNmls2lAcqZTQReFq8TEGuX2NGQqRD4GQrF4Z50YJOoJGstsT2/fGIBnZzFCyuC3aCMIRN4hDVDrBUq1qAc9IqATAW/OAbRck7TX2uRD0RhUve4TqR0qognDlZPgUpDX+IoJux2WZiaod0WFhuCbrcYG8njFHNMjAwStVuEOsIpFSFIlKJELbg8vBov98UCf//pz/J3n/0sfi5DjOWJg08zMDaGshpXCa4o+op97Nq6nawX4DkulaUqx0+fYme9Ttjp4vhp+E06nsYaVLqFfi3AGPDEQzkOxvXQxCgMjnSo1aY4e/oweTNGaXyYfCGL0OXD3/VO5Lvu4szyDB/5q49y9MwxtKsxbkwg4KdbUleB1i5GO8Rx71V9z6YBoljTiSIKjsVYg7WCshqNIqpNks8G+EE/cclgHR+lXHxHY7MFugs1nLBCQEwYd5l5/FO0l6YI222UF1BtNWm3WpSkNx0ioOOYE8dO4KmYjRPDbN61Aas1YeMsj33hCJniKDfd+BZed/u9fPYfPkve73Di7BleOPgMmcDFdQWjNRihG8U4rnMuUsZzHKzROGuH3p7bFQSeh1jLhrFh9u67FmsNpb4+vvLVr7G0WE2FOYiNMRiyeQ90RBg2OXn2MLX2LH1DZdymT3VhBgm75F2Xe26/g2888nRPGor+KNfvKPPMwS/zpa98mb079xIol8eePsKZuVPMzS5z6833M17aSdHvB0chapbpqRmOHz/O7t27ef2dbySfK/E7f/wRlhaafPB7PsitN+/hhYMPMuSA6/gsrmHmyuVzaCDSXfr7ykTaUG3VyeRKvOG++5meOsXMqSMcev5J7n7DfTg54do3301rcplnPvEYO/xdXL9lH7MTATf2DdDxFMtTy8TVNvW4zS//+kfoHxlINPseaDY13XQxXgn9NGm8tSExsaxEg6yEHDpLs7TjiIxfYnq+RkwiXHXsoFRqz1YK5aZx4qIQq7jtpku/iiDjRGjAioNOY8SVcWlXY6pzFR792sNMn62xtDCDNQ2Wl86yvHgc6wZs2XEDbtxibvIAP/Kvf4yf/oXHiBePEYdLvOsD7+fd3/thYnclwuzyzAqv2puo5+fnUUoolgp0Wx3iMGJuZgolhsD3cR2PTrdDLsiwYXgUt1AgX8hjHcvs5DSu66KcNHYXwCYT16R2ccf59rerZAMvcZ77EHoaqzVikzCq0HRoNg2L8xX6fEsUa5wg0SiOHX6Wp545zuSRJn6QpataGJ3ECVsraAvGCNqNsdoh1r3twDfuTF5moo2lr6+IQyd1vAhiLNmsS254C0GuhIkHaHViXOXhZyy//Vu/zf5t/Vy/9yZmnn+IfP8wys8SK5921EB364kQdR1ajTVs4pDG+Ar1WoNopIyoPJkgx8jQCM899TTVyeMMbatz0zU3cdsd9xC2l1gcGiGMhTOnj2JNROA7SKzjS3OVAAAgAElEQVQIwxAdpWGFYnCVkyZorAFZ+UcQa9i6aRPvfs+72LljO6W+AuX+Mju2beP3/+CPqNXaicdTFMoa+stlgiA4twgkPKrJ5XK0m8vMTM3Sv2k3/aV+8rne7wP2pYC1LsvTFR5/9HHOnJrDc4TKwgyx7dDXN8HM1AI7R+/ARjGVpQae4+MoQzdsMTtbYcOm7Tzw5Qfw3Cy+m2X37p3EpsPE2Dizh2doNiO2bN7Sk45Wq0WkY5QI83MV2pFFi2Xrns30DQwRtluYqENlcY52o8rQyATtyTke/szDPPzEk0x3mrz1mr1ov8OphSZOKUuunId6B9cRJBPgBAGe7j0yn/nyo3S6nXPmkhVBbknMK8omGQ5dE+MYhYlDbh0zTDfrTD5fQcQhTu3dSrxzu3YRdS7hRzkOjnL44HvvuyQdrgsOSYigRWG6MY9+7REe/cqjLE5Nc+rEM3SiCuJ4FPv68TMe5bGdbNx8G9t2befwUw9Qq0dU613K5WHyBUWrehalDTlHaOkYx/Mu2zT8qghxAaI4wsQx9VodE2uIDYuVebKZDM1WE2sMuVwWbU0ioO0ohXyegfIArXaLdruVxvxy3jCWBt6/VhB4go4t4hg6YtHGoKzCakMnNOhIaNXrzM3UOXnyBMvtgKMnZ5mfmub5AzP4pQKFcRdRfuJa0YZYWyJtsaKxrkYpuSDp4ZtRKOTPZUZm83k6tTaJgTA1cYRNZg49yaMHDrD/3u9kfGQURxnOHnuehl5Gl/eRDXyCgY2E7WWy2RLu2GbiKKZerxA4DmODW1hanu9Jh+solCQaSBSFTE/PMjU5ibEOpb5BTp6eIzYOo0dOcOOtb2D/627B6C46arJ//6088KW/54nHH8BREY4Swm7Sp5LGFmut8dIMuZ4QQeGiUGR8n9tvex2vv+N2RkeG6O8r0WrWeMMdt/HgV77GQ488gbg+BsGxkHE9HASsIRcUINboToti3yjZIGC51iLsdMkG2bWFuPJxRNi+cTvbNx3m7Ow83XabciHDnftvZ2Lj66hWFIuL8+zbtZPKwgwLs4vUq8tEYcj+/bfQ6UbMzM8gKHbu2EGjvki73WTryAALuQLjQ0WGxyd60qG0IWq2ca1Q60R4mRLduI0bBBgLOo7IZzNs37qdwYEhivkilVqdMOfz7OkX2LZxHFUKaE4vUy0LgQ3xgz68jIvTsoxsGEYpwVlD2ZhZqBBHUeIDNElMOgKu46QZmi6iFMYxZEQYH+1noT1NOxJa7RhR9lwCmuvqFUdQkvuQ2sRFrR2d7aYZuyKgRPPUE4/zqY//ObWlWaxtURgeYjC/DScYQjkFgkCoLZ5lYvsmhkYyHJeIcrGfY4dP0G3W6OsvsHPDPs4eeoHnPvdJOl6RkAz9I+OwY+ca1Lxajk0gn89hYk2n2U3CvhDm5+cZHh4mikKiTpd6wyeMYnzA6phCvsDo0DCFfJ5Op03U6ZLxvVR+uyhreQnhlZek7sI5/qKsUC7PMXY50DpCUCiVZChqrYnCGDR024Zuq0M9bmBaDRrtLrUoz7GT01y351peOFSj2dD02SJRDJ1uI0lDtpbYJNyYRGcIjtfbjOG5TpIXpBKb9Ip2k4TVKZbml/HHd3By+tPkn3+QzaP/FC/wOHtinvfcdzebh7fSac0ysHkHzbkZ2u0ancgQaYvrKLSxZLPCloFre9KhxKYhvRajBRGfZjtJ40YCbDDIpk3bGZ/YQbfTIW7U6LRalAolNm3ezbvfN8jY2ARPPvIVFmePIY6g8PCCAItBWw3mMpKOzEpssWVsdIQ9e3azefMmxkeGcZRQXZjGhm1uuG43zz9/kGY7xACuY3CUwRpNFEZkMzl0HNNo1MjmB8jncwwNDtLttgkCn1w205MOsZooDBksD/Lu++/j4cee5vChY1y3Zxf7991AvdpHLergukmmpjEdJifP4DiwZfN2brnlVj7zuS8xNT3JTftvZKm2xDMvPM5td+xlpjZP6CsmxvrJ5tfIUESn4ZAxW/ZsohsK0WxEuVikW6/TUl0yvsPWnXsYGRvHikI3l9l/237ue+ObqB89g29jvIFBwmIN3Yam1gxlA/yWplAopBm1vSdv4Ac4qQlk5WiHczQ6Dp6X2JJvvmYTIwNZOqFm7tgyTrbIbKtLGOtzY78SrbQyt22aqARJJFMvZJwVRcAwO32GZ5/8Mje/8XauufUNZIKAv/nPf02tEaIcg47qhPUmgxlDyZsh5ynCbgutHbpdIRME1Ootcr7DUqXBY489xTW7r+H0VJtT7iS859tQiK8Iw62bt5DxM0Q2yaSzCN1ul+npaVR6BoK7km3XjdBRzNDQMMp1iaMYYzUzk1OUirsxWhMZi6cNVlYm6OUL2tUC2pjEMXjJcpuE470SdvcIQWxi/0cSG3kcWXQXWq2I2cUlVF4jWc1ytU3LukQonj54CPFcJIpwyICFTnMZx1c4noPnued8PivnefRuv04Wk3NqSLpN1RpjoRu2GOsf4v433Ut9uUkU1fFizb7dG3GUwelW6EYWbAtxDLEbgGpi4wi0ZnB4lOrCPMObh3rSYXSyCFuEXTfcyqYNI3z9q1/EWMhmc9xy2w3su/EWbrj+eqK4w/z8LCaOaTVb+LUMuXyOO+76Du5+/b38/L/7IRwfYp3ECadLAxqDOGs4erXBEOG4Dlu3bmJoaIggyBBFEaHRYCJ01GHLxCjXX7ODZ597nmbYJhs45LIKoyOiMCSfz2OModsNscYQdTsUsxkW2m0KuRJDQ2v0h40Jw5Dl5SbX799D3i+wb8c+RkZGqSzWcMjTrFbQ/R0OHz7IQLkfpYR3f+d7GBwZJ4pinnzycURplLJ86vN/z4m5o8zEi7RqM9yxbw8ECnF780eM4AQBxnUY3TrGkUMn8DIOsQ5p1Cu4JsAfLjA0OorG0mrWyQUeo6NDvO+938Xf/O6f0GqHbC5v5KzERPkC1XqWQk4wrWVEHDzXWzMio9vt0ukkJ/laDFqDjeNkcVaJeTLutNi15Sau3bWZLzz4JPmBIXSjjmlX6JoMnSiJ2LKxToS4UghCEARImiy01tz2Uic2CPWlZcKax869eymWy8zOn0TpWXIqQuIOUauOYx02b72B7btu5cRUhemlMqFxePDBGXCSDODlhQZYw8TmLOPbNzPdXKAd9g79XMFVFeIrNiwB3vzmN+N7Ab/3+3/IsZMn0FjCFSe5rPj9odqosTg3y+TMWcLYYLTFUw6DA/0USnmGhgcYHBxm6/Agm7bvodw/QDabZWzrZb2Z7RxdSilOnz7Dn/7Zn/PMc88RxxHKQpAJCDIZYm3Qccw11+7mB7//+xkdHsGsGvAryQCdb1vCTojTgWxWgQZH+cQmsbWdmJshKuSZijp4jodxDbHyyBcCdg724zmCQdHsBOzeWmS+fgatQhw/DXiLTGqi6a15xlHiTAaIwg6tTojVEWJAHEtDK2ZfeJgNwyO4g+NUn/kMsZcnjENGbng9Xe9a4nqD1vRR4mCYsFVlcXmKUl8/GbdMcWgDzlKRmdNHe9KxecsuKtUaW7fvYPd1+/nC5z7FUq2F2Banz87xH3/zh9i4dTvVWpVyocimm17H7PQZup062cDBiGa5GQHC8Mh2jh15Ds930CbRtDJZnyBI/np3iMHxXFxl2LljK2Nj42nkk8J1hYmtOyn2VxkaGWfPnj3Mzc3xwFc+Rb06QyGXx9oO9XqNuF3HWkulUiEI5mlUFxOTUXGYfLbAjTdc35OMU3Nn2LXtGq4dvI1uq8qODaOUcjEHD51CMUY7rJMtCcNDgzz3xAlOHDrGhz70vVSX6xx47jDPHfwk97/9LWjd4eHHHuHIkWNMV2vMLjxNPm+45cZdnJmfZUGtkfykYsRqNm4aIxCXu++4lUajQSGfZW5+Ct1J7PthtMBypcn+m/fTabTpRDVspsNy3ESmfDaX2rhjG9HtGmF+mvGWsNgI0FEIl5GE1Wq30HEMIuzfMczUYpVWRxNqB98R7r1lJ29/6xtoNlo8efAo5YF+nJEyA7U6ga/40//3vyGOy81bNzGVH0OUwveDxL8mEIaJ72KttPsAnfiLRFHM5mlVQ/7uv/wJzWYDV/nkshvAU/hegPIcTFzDZi3PHHeph+OE+SFslCWOO4ThNDnfww/6aLaFZw+f4IP5+8k6Do3W5b3J76oKcUmzlpIoEuHuu++ir1zmL//qYzz25BO0W53kOtIsPyAyGsd1aYddUA5GoN1t0Z3r4i4pzs5MoiPDnTs2cnJqkUIuRz6f5/3f989eMn2f+9zn+fVf/028TLICNmtVHNclk8si4tDpdvncF79Ap93h5372ZwmCF6+UL1WQt2NodcHpgo/BdQVxBe0oXEeIbAPrFhAT0KpXGRjvp94KyQc+Vhpo5aPcgEzWxXN9BvpGWGidQRyD0g5iknMu1sIzzx8nF3jkAh8hJoraYC2uUlixTIzsYDksMDy4Hac6STtycU0Gb2SIbnURM9hPFGsQS9ju0q7MoOIG2XyObDYgjiM0Gbph7zzzThjjeR4bN4zz6Fe/xPzUJH7gobVBG+HLX/wct995JwMDA9S6bR576EEefvArzM6cZd++PbztXe9hbGiM6dl5xkbHOHroOUycmFGUWKIoSqJ31tiZJJ4yg+coioXceZurUjgKjAoolgcQx6PZbjNkLWODZcL2HLlcQCbwabfbuDaJkgjDkHa7TbvdJp/LJjlD1tJfLvcko7rYZDY7z8TEIJmi4vCxGR567CDaKPoKBarLFW688XpatSbFYj9vuPs+OmHIseNHOXzkJO//wAcZGunn2LEXmK/VWa41sV2IifGyLs88e5BypsjG4dGedOhuC20tbt5npH8I11WcnJ7hROMow6MbGciUsV3Dcr3C8zPPc/bsJO9429tpxxFff+Qhnnn+Oe687g6iqdPk+zeSMxXuMVX2K8WX/QLVhWWKQ32IFXrJT89xUSKEYcT/+P63c+L0aU6dnmFqep7a8SNcu32cqeNHmF6sU2+0KZRLKOOQywZ0lirceO02vGyGkWyWgaFhlONTa4VYC/VGC8930SZ+UebnpRgkjrvUa1XyQRbbhbzj4nghzfok2cxZTKdBfb5JsxESeEW6A5tpdM5igzK+NbSjDmIjiq5Bh8doR6BUFtt2CcMmY+MZYru0Bh0JrqoQryzV8JRL4DlEkhysc/3e68l+0MfGEQ8++hiOctPswQiLBivEscZIF8RJsjZFGBgoMlge5s677uKxRx/jlltv4JZ734UoiLqX/y7alRPqTp8+zUf/8i9wHMs1O7cjSjh+4ihY2Lp1C0q51GoNFusN/vvf/T1jo+P88+/7UGKvvUIox8VYQ9SOiAMviUzBJIdPOQBdjBMzMDqKzhUY6CuBU2Ohs8xgQZEzik4rxCBkckP05cpUu3OEpo1Yg6ssCnlRVtvF6UjCENumQ86z+MpLs16FWEcszC4ysGWUUEMhyJIrb8HWQQY3IY5Hp9HGhiGqNEpr5hlM3MLELfyghNUxYbtB38AY9eVcTzrm5ufYu/daslmfudkp4k4HE7loN8OOfTfz+GMP8fyBx9l/y+1MbNzA8tIi7UaFkcFhBoY3Uam1OTv9HLV6lWtvfB1Hjh7l7IkjBFkXY2OMUXS7YXIAWw/EscV3hSArFAtZTByhoy5hFOJ7LoZkB9TudBM7qgUHD1cyFHJlspkc3W4bL+ODeFTry3jZPJ6XQYI8rXaHfByyxn6ATRu202pUeeGFJTZtLFNtdNC4jIyNMjY0yAvPNXGty6bNm9i+dRtbNm/hwMHnqFYb3HXXnYxPjDI9O8VyvckDDz5GOwRRLkZ3UcaiOw7PHjnB4N2DPeko5EogDv0jJaYnJxkcGmBiwwQnjh7GjVu0ww46NhRzJSajOR782iO8813vIWNcJqdm6XQ7eKFmvmrZ41lM18fWR/l6LaTRMJw5fJZ9IwNEJl7zzD4R8HyPSq2K6yq0jvACh3hpiWakkFyGXN7SV+6nG0eU+kq0Wm127NnDlj17aOuIdrXB2EABYyBsd8HPMd8VtE38UsuV3scQLFeX+MbDDzA3M8V33Pt2ctkaCzPHadbnyDgQN3PU2opQ9+Fm8miV5cCBU/h9bbZdfzNbRseZajaptmr0uSFzS4dptJfJ5zZA0MfmjWM8c/ALzE+fIXmPc29cVSH+0Y9/gr58kZHBMQrFMTKZgMWlU5w+/kKibWlDPpfDUQ6Ndoso6iT2R2sBhVUakyalGAMjw6NsHp/ga92v89TBFxjYuJuhcplWp8Xozk0vgTLLkePHCfIFrtm3j80bNyXnNHjJ+SSD5X6s4yB+QOx6GA1//fGPs2nDMO9813vTzD6VHJP6Emzxvu+QzXq0Q0MUO8lOw2iiOCK2lqyvqHeXMcEY/bkhPNOimPeZa8VkjUPG0cRWo42lVq+TVxkybp52u4V4yfG2ohKB0wv9+QAl4DpC4ICTOnxUakduKUXHgBEXU56guGkfzcOnyYxtplFZpLE8j6MUXr6fdrOVOP2NIo4ilG4Tttt4BY3r9p6i27dvZc/u3VQqi9QbNbQ1tDodtu7awnd/z3fzmU98nNHhYSY27uDUqRMoZbjz3vvYt/cGSuUBqo1a4uDyPXzP4a33v4v//jcfpVKdxfMd4ihGWXWR056/iR1QAoV8gOsIWod0O22arsIplYhiQxR2adRrdDsdatVl2q02rvJRkhzw0w1DHNGAIopDmq0GuZwH3YiuDgmqFTqt3sLi7OQMi5VJFhenWVgcoxOVCfwi7VbE8mKVO267C89xGRsbp1qtcvToMU6dmmLTxm1s2bKFAwee5eTUaU5PzXFmegmLD4SMDfezf+8YpWye0s5+duze3pOOieEhmpGmpIXQF7xuk5H+fna//k7a7Yg4X8brcygWStz+hrvZsXgtYTeikM0TBBlymRyusUjXIodq1Kstqm2PWCdn3T/z2LNce9s+rOrt2NQ6iREXEb729acIPEW1uozjuWy5bg+7d24lkyvSaTbwfZ9Wt4MfBMRxzK5dO2m3O8zNz5F1fQwKazS6G+IGPvdMbKCQKWAtNJqdnnTEccTc3BmOHTnIHdffgu8G1OoRWufR2mV2yaLFTaJo0GRyMf0lh1tfP0Rxg3D4KZdlD1oqJpdrMjpUQkyJyckKnit0O00OHXyCxx97DPilnrTAVRbiRw+/gLKQzxYpFIcIfJ9mfZa4UyPjQsF38UXjBhm0zWB1hHF1YhKwSRJHJp+EXY2ObWT//lsIfJ9qbZlT4QJf/IdPJGcjeA5777rtsmhSSlFvNOjrH+An/vVPE8Y6dawmQfyVSgXPccnmciil+PpDD1GrLnP3G+7FmpBulKRmh90Q13Xw05MWLweub8nmFabrYEWINHS7lji2Scifo2mbKjO1KUoT20C3GMy7VFsZqvU2KhtjUYTa0DIdYkcoFPrphE06pp0cymQUoe4txH3fw1WC5yp8VyVCnCRxyqgMKt+HVT4zM9Ns3Lwd3WwzOztHX1+B6lIFRCgODdBpdMi6Id1YyAZJJEEcWUJRSKe+Zn9s3DRBGHU4cvQQzWb7/2PvTWPs3LLzvGcP33jmUzNZLM688zy2ulu3pW61WrOhGJYDS/+CREBkBwmSwD+SAIYTG0YGIIgVwXZiwI4TREMsyXFraA3tVqu71d13HslL8pIssoo1nvmcb9x758dXZN+2dQ+v8uMCAvgSBYIssrBq1/etvfba73pfjpRHMfmM17/7jYqFMR1z6/o1nCkYJhNe+IGXqC8sU5Y5w0EfISQry2tcu3aN5bUNPvcjP8bvfPk3SdMh9chHOHFkmvHRUMIhhKFRjyjLkixLmEzHWGcIAp/hcMhkPGI8GpKnCXk2Yzobgazi1VqDkyRpCrIaJknTFOvGyCxD+SH9YY80m2+3uXt4m69/4yssLLWZJhOOHX+aKKyTpVNkXdFqtNhYP8Hh4SELC4v0en1azS5KwN7eHgfjHl/7+p/y5uXrOOnTjDzOnl3jhefPsVTPqHkdnDasHZ9/fxQFPqPhARQeq90WzjnSXo94eZluuwU7A2a3t5mef4D2sSanzp7DGUdRliwsLFCr1UiFRTpIr/aQ2gdV4smq3/72K2/x+Z0vsrg+/6L3bptDSv74628wO7zOhVPrTCZTtjdv8uAzD/Lk0y+wvLRUcfWtJU1nBEFMaXNMq8mxtWVi30dLXf1s84K0KOiPxuxvb9Fst+m254//e37IwsJxbgVb9AdDsDXyrEHBmDQ/PBK/qtgr1glwS8ThAr4ckU22GYxKBpN92q0B2o349Is/wM3NPW7c/CbCSzBFxtrSIs14/sn1Dj7RJH666WFLQxhl2PIGvgxoBoK4FpHlmviZh8iKkpuHA7q1GgcuYeZgkrgjHQJHHPkoBOfPXaAsCy5deg+TT+gsNiHZZ5LOyN29elrfjzTJ6B0O2d7r4fkBYd2vZEudYzIeE8YRQRggheCJJx5HKcXC4jL72zf5zS9/helkyMb6Ko88/BAb93ghPgzPL0BAEVSCXUVpybKKURF6GicsVhX0kh1uj1usxjHnV0twJTsz6KUFfhBROvC15mA04UTcZKG+wMFki9wI8kKQzp+qJgwDtJJ4SuJ7uhLssSUohQzamOmQOI7ZvHGDm1eu0F3e4PjGaSZTg/BC/DCgLBwH166CqTRTpKfQ2sNaHw+fSX//7p3HRyFJpyTJmMlkTKMZQ2nJ0pz+7W2++yd/QLsbkYURuzcv4gUhaI9L7z/AB9e3cCZjOh7gnODc2QvU4hbjyZgLDz3JaDzi63/yFZaX2qSzhPF4/oZiTUno+9RrPmVRMJmMCAMPpWAw6HFwsM/B3h5ZMsWanCyZkaSTSoY0CAiCkCwrmCYJUissMEsT0sIitUdQs5R3ZsXn4JGnTzG1D3Lq5Gnefm0bazXGGJaXupw5eYIiSwgCD60029u32dy8yfH1E8zGE3Z3Dlg4tsytWztc39ym2ejSiRVPPnKKZ548i2d7XL64SdyKuXbtg7lx5E6SCkHYWUYH1UVgrVkxqjYvX8G9f5Wg5vHOWxdZevgJnv3sS5xeW8Maw9LiEkpKRtMEz/dpBh5K2qMTtUQiOdju8c7r7/LS8ZfmxlGWVb9aSkk9CpmWlm9/82W29m6TpDP+0f/2f/Dv/dUR586fodFo4HLLaNynHrc4ODhke3ef1Aja7Q5aOXq9CVkmGM0Sru3s4jLLwsICThj++7/7X35kHL5q8ennf5T1xTOMDneI6hntjk9vFCCIUWWMEx5OZghZYqyH1NCstYk66zRrA2y+w/7NIasPP0DpApaWV3j0kSe4tXOD11/7Nul0xKc/9eLc9biDTzSJj3cvVTKythJ9QoKVBg+HtQJrK6GiwoyIlIeOfE77AfsmxShJo93B5CW+9rj63ivcuBJzY2uTWZoQhCc4vbiE9O59y/1hOOd4+bXX+Dt/97/j8uXLAHzpx36M9dMbFHlxVzzpzuj2XXMABwfDPr/9L/5PwjBkMBjxUz/1E/zD/+V/pl6vf7xq3DMUU0duSup+5dIjpWAyUYwmFmEFQSSRasrw2ltc0TGfPfcgP/RkjClTDgYJL79zk1u2Tr+xggAO9vdpTw7xmgG9iSHLSu4lY2iFBKUwgmrjEIDnIbGQD5EqYPT+V6l7EVNb48bbe5TTBL/RQPgB6ajPePsStXyb9tpJsBYX1hjPUibDHrPS0V5cpX6PwuLxR55iMOizt73PIDckRUKtEdKIJbVI8cKLz/PqK28xHPRZXVtgYbHOK9/6MqdOPcwTT3+G9gMPc+vWLQ76Ax54+GGCNGA6GfLw4y/w+BPP89U//lUGH1yiNPPvTLRwLC7UqNd8ppMJ+we7GJOT5knFTy4Sbm5eq1gVtqTIU7J8TK3WQAhIZglWSHTgk5dF1TNGY4sCISWxEpTCMp7MVzFMyxu8+OJjNKMlbt+UXHjwAm+8+jIXzpxlNu7RWTrFO++8TZ5lTIdDTix1uHz5Dd5//zo/+NIXmU0NF849wpWbB5xar/FTX3iWhx46QT/pkbuSl37sWbZu7rB17WBuHOcefobZ69/h3KNPcnx1/e49knGG9JGU/af7vHvpIua9K3ztK39Kb5DR7TY5deIkTz/2KO9+5nEOPzgkNprQ93FKHvGyLUI6IiP5zh/+Gx5/9gKrJzY+ej3SFHXE0V5fiymTDYhW8Y89xGQ6pXXsLLd7GZPXr/LBB9c42B8hsEwnU/q5IJMhWisCX9NZWMBayDLDbDYjDALK0nJ99yqBP7/t99U//GUUBmFyPOFIsptMkxHWBpSm4Ie/sEo9biOIee21q2xtTdnctvzz37hI4F9BMqW9lHDq1BnywTa7O31iLyad7OKJjH/8v/4y3W6b8WQI/A9zY4FPOIkPCounNQhBHPkEnga8ox6sZiFsIH2P/eI6x7rLZB9s8qlHHmC5u0xhLXEjrh4gBO1mAyM9/sW/+m2u3JqwsX6Szz7+JEqqSlLyL4BvfeMbfPD+Zc6fPUMY+rz71tvUmw3yskApVR2P+R77xB3pNLx/5QqnT59mdXWFb37rm+zv736Mm+3vIR0aigmQV5OnfiBQQTW16IqC6axqh2jtIzVM0iE3d2f4oku3IXnwQp0zZ1f4s/f2+b09xywICGWNwkbYyYg0z5FSVNrP8+JIEkxRVVYu0EdayRU7RUqfssyxJsOVM3zToyU0N979BmPjs7B2goYqEJObNI5vkGUFWIcQHlI5wloHm2YIpxkdbM+N4+BgiyLLWFlu4+uS/d2UOJL4vqDWaPLrv/a7WCfQvubg/VtEm7d58rFHuX39IjtbWzz+9Issr64jtabdahGHCmzOJJ8Rej5PPvoCo8Mh2Wxzbhy+56jVNL7vk+cZaTJh6iu0EqRpinSGyWhEkc+QWJLZuGLgGEiTHGeHlEWJswVJUVJOJ/hhDYfGlAaZppTGkCXzTxLOB+cAACAASURBVCbtZgebKSaHKWEk2D+8ycpyh4O9XWazGd3uBlmZMZ4c8Np3vsNkOKK+3OH2/m12DnbYOH2aM2dOsXHxfX72xz/N80+cRGoDnkR5HvnUUMwkupyftAb9AcNhwuaNmxRZRa3zfR+noBSG1bPnMI02Tzz7POcvXuH1197kzVffpFNr0W63+PwXf5hv/s63OLiyW03QHtFZ71rfKcX+9gG7N3fnJvEwijBH1fi0APwIPIlQPpHWDCcFW9s9mpHHJCmZ5JW0cn6kKFgaSxCGRLWYsjCkaUZRWMqyZFJWcyhatY40VT4aZ1draGnRwhKFHnWvzu6NP+PGZo/Fuoezjt3bN5nNMtK0h1ITKDdZrrc43DskTfqcPn2cKBVcvfoOgacI/Rq7e7eJmopuW9NtWc6cXJ0bxx18okn8hWc+S+B71LTPUr1GFHgoP0Z6PghFLBQFgje2fo2VxXWWFlbYWF7m4TMnKfMCZyves7UWYR2pqQZvlhc6LLVbSFscDdrdW6XuDoQQ+L5Hp9Ni/dgazVaT3/p/v4w4mlystCQFd37dEU1TQrC3t88DZ07RaDTptNqcPXOW9j1oYx9GNhTYTOI7jc0sRjj8yKEjgexqxiOL1hKcIDeGtHRc2zxk2Jvx4OlFhFBcON/m06JkP51yuxhDCHnYZpZmKJXheYIwnv9Qego8JZBKoWXVUqlOHA4pAmw5xjs63EgPBAVGCkZpRFyv4yVbRO020oux+fBIz1lQ5gVZlkEYMRj2MG5+X+eP/uAPcdbiaUkjDlDOUCYZjfoiSWbx/BCEwgqLJxymLLn47iWa9Torq6sMD29x4tRJlrprlHnJzvZtrly5xLXL1ymynPW1GGvN0UX5RyOOFVGkK3NoW5KnMxItkdgqiQPT6Zg0GaOlZTYdURQFzkqmsxTnBNPREGdKstKSlDNioZCyumRL0oQoikjT+XG8+/p7BLLBxvqD7O5fJ5qOObV2hlmSsXfQ4/wDBTsHW3zz63/EcmuRZ198ka+9/DKjJOe9K1c4+8gDnDq5zo+89Fl+8FMvEHs543Efm0DkR4RSE+uMqZ5/Mrl88S2SPOH69Q8YHRwQRiGHhz2SyZhjy13GJ8+x8eBTXLp2mZXjyzyjnuT6lU1Wly5zbP0YXqhpthvsmdt3JySNNZXOvBB4yiMd5WxevsUTP/DcR8bhaX1X22+WlxTG4HmaoizBaTzPoyhLhjOL9CNUUDIdjwCNF2iMVQihSWYlxszI8xKldNV6ASaTMVEUEYXzJ2kfXD6Do8TajDSf8OiZGq2fe5Kt7dsUzvHH33qZb371dYq8pN2ptHRm2RSbBkynU6wxXL06Znv7Bu12nRMnFwkiOPPIeVZWOpw6toavq/bmx8EnmsSfvfAYZZETCknDUygFRkpKJ8gLQ382pjdJuHFzm+FgzBOPPsTL773H9e2qQsvynCRNSdMUh0N7IVsHPY6trbJ3OOK17Cq+F+BpycftTDvn+Jmf/mkuXrzEeDxmOBzypS/+CCsrKxVD5ShpS1GxTzyl0UohlEJIyb/6rd+kd3jI6dNn+IVf+IW/0HqYXCPuKNsLjbGG0hh04BFEEhVW48V5mmJyB6klDmtMpymFqVGaGtOJhjzhrz3dZW84YcQCX3l9j5EZUQtyEBbPm99e0p5GSYEWAi24K9UqdYiQlfVYtXkaDJVpxPLyMaw6jdYBk+0d2mefIJ8cYJwizxOEM8xmU/KiwCQQhh5xez5jaNAfo6Wg1ahxsNen5kuWlro46bN5+4C8tHC0ESgJvh+QG8MkTeH2TSazKXgxZx8WJPGU6WSMlrLiNV+9RuQ3GQ737kkgajRrhGGE9nysrbwii7JgMp2QZznCWfI8qz5swXQ2pShKyrKgLHOS1NDr71fPiVexMGazFKVK8iOuvLWGexTivPat1ygyyac/2+DYepfBIKMoEtaWjxPGMe2uxym7jC2f5NnHn+PkiQf4xb/932Cs4NKVW6xvbPCZTz/P6kIHZQuGgwEHB/vkRU4QFjRaq9zIDvnmt17hb86JIwos49yhvZJOzRBFllhHsOARKcd0sMnltx3GafYO9wnDGskk4drVa/QGh4xmI4IwQOmKby+FquhlziGUorSGNC24dvXG3PVIkilZlleUY6vI0pysMDghq0vMrGDr9gwvqJQrh8Mpk/GMMAoJAkkQKJwwpFlGkTkODgcILWmlBasr1aWqMaYqPObgyo03sNagPYEQJUoUNJqSM16HGzdvc2ypyU/82KfwPB9PaXzPQ/sOp6o5iFqtRrvdohbXOLm2TBBqJtM+SjvyLGGls05RZJh7+fcd4RNN4kWaM5tNGZucpBGhJTjjSLOCWZKyc3jI3nDGJEk56PeBksFkii8lWEdpDIWpRpGFFnjSY5Ym9Ho93jKXOFxs0ghDGuHH524757hw4QJPPfUUv/d7v0uW5fwX//l/RuD7lMZQHtGanLFHbImKgqCkxPM0r778XXzf5xd+/ud5+umn/2LrYQTSuSMqmn9UmSiMqbjKcV1VovOywDjLSqvLyeYirYU6Zy+so7GkxmM0G/LQUpNTKx2S+ineHYT00usVj7a0VV98DrK8RIQ+WlauJuJIM0WUBXk6o8jLyqFEa7Cucj4xlprvOLh9jcWFDZwXH2ksV+a4k2GfvHD0ZwXJZIt6pGm05+tFh75COkimCXEg6Sy1KH3N9s4+RXHklkLlGC6P5BW05xFEIaNpSn+2z+nxPs0wJfIEjbUarfoSkZ8RRynvvvsKWVHePc5/FKLIJwxDPO3jnKEsc7JMkSSGoihQQpBlKVmW4kxGms7Ii5w0TQiCCZNpwWDUI45r+LKOcVCkGYKUNM0AR5om5OX812+h2WHYS7hy+TI/9fNfIkkNt67scuXyJQLfYzjwCTzL+dPHmQz3eG8yo9n1j3Q5Rnz7z77O8fUmsSfZvrHNaNKju9CmVq8R1TQXr10l7i4QdeezQs6ttDjerRH6Hu24TpZmBEIRBBFgqCnFzt4HjGYeM+fRXfIhn3D5nbdRcUBvMuZk8zSepxEuQ1oP6QRT4dgZjRj6mlmREm7Nn+hNkikgCcOI0aiPF/g4LOpIRVJpg0OivKASyNO6WvvSIqXBAXmZ4mmPVruO8iRWWuKjNk3llCXuOf6fliM8T9PsLJDnGTgfT2iUtDz4UJeHnnyKdjMAa7F5gS8rU3gnuav54vs+WZbRCNsIIPFrlSlLSxJqj0xnZMU9dvkjfLI98XSEEA4tLIPDHoV1pEBalKR5TmoMKvToNhrcdpblhSU2ji0irAPj0FLhqcouqxYFxEFQ6Y5YV+22niL2PO5pKcmHRf8Fnqf5T/7WL/G3/uZ/jLX2z+1r37nU/PDlphCCH/3RL/3/Xo9hUlKrSYJAU+YGgQWjwAjGg5KkhEbHJ1YhT546R5h5LHfbNBp1aqEPSqKDiKCzQU/HvHP9FlFtl6jYYzYtEMIymYG7B8VwNBwzHVf6yiudGn7gobRCAp5UgKI0JWmaf8/AtShZ2FhAhxHZLCOZDQmDmP7OFuNZgRM10rRPIEs2nvgUh3u3MOX89oGyFt+XLC938QK4vX9IYQTWgNYeQaAq0+OjBF4UOZ4nyIuMl176AqdPn+bb3/lT/se//18TBJrKU6VSrZNS4vse9biGtfPvLboLdZrNFlFUR0hLXiSk2fTucyGcZTIdUBYJwhUURUqW5Qxsj6JMSNJxdQqxOZGTaD/CWMts3GMyrkbxrbE4Mb/Y+MEXn6Zd7xJ012ksrLB5c4taEPDQ42fZ3HyX6WSHd958l6Zq8Oorr3EwmvA7X/lHJLN98knJdJSysBITxg3+9e/8G1777tv8lZ/8CfZv3eDlL7/K85//ArevXOLmYH4FbJIU7RzJdIZLCqw1KKXI0vSuWFxdQa3jOPvMpzicCGZJwatvvM7O5ha9wYi9YJ9jrXV2vTUyL2amFIPpAG+1jtNtfDfk5uWX58YhhCbPc6QqsVZR5oBVTGcpCLh6dUgYaeqNCKUUoR/SaLVI0pThJCWMayjtY4wjCBVlqSmyEmHAULVmkiS5pxLqm9vvoKWiuJIjEdTjFoHnEyoPH8mZ9VP092YIC7EfkOcpICgxDIf7BFoShxHCSqwbUBqLk7LSJ3cO4Qo85WHyj9cW/kSTeJIkFHmKMSXGWCyCLDfkpaEoDNMirXqiSuBMiac1rVChhSLwPDxR9Yk8rWnV4moYpSxRVAwSfaTf7D7G5eIdo9QPW3bdedmDIDiy/Ph+y6d/G3cuZu78/z8P81gqUeDh62q4pjAGISpRH61lpSaYlchEUbM+Ikkpy4KTpyuKo0NUzvDKsbK6AErQ6i5y8Z33EElO02mm+QzfWcL4HoI+UYR2llmS8/qNw+rkYR1B4OH7AccXA+qeouZ51YRpafC1QFISBT6T/gAvUEyNpBCaWrvBbLiP9jTWwu3bO9RqCwx35yeLjRNLnD1/ko2T63z75ddodwR+EKOkhxASpUtmsynT8QxrLUoJPF8ThgE3b17l1Ve+SZKOiUIfKY5cy++4m0uJrryzqtPNHNTqIb6vUboSYqsG0czd52U0HuGAIAhIZzkOUYleIUhSQZJlIMXRWhVoP6IsCkaDAelsetTVVYzvoY0xnk4QzpDPElZ9yVJrgYaJONza5d13X+WZ5eeo1dvIFJ579jmay2vU42WK2SF7vR1G44SSgqW1k6yfO8GgX9DpHuPS1U3GSUm7UadIxjx0fr7LvKd9HODpAHBo7X/PK/KosJEOsiRnPDjgqU99ga2dHaJayLJbIEsSaNY4rNcZxUtINBQOR4rEp5QlWhY88dD8OKyFoigpygnpLCWKY9QRe0xpRS2O8f2Kl5+lGVoassIipI8XHpmdlA6lFL3elMl4jFKVCXvnyBqu+t7mPx+r9TWUkMQLEfUwJvAiQj/AV5p0OqPhNSlchhWWIIrwW23K0pBnOU44JuM+uRMopRmNE4oyR3kCP/Qpyox8llHkJZ1mZ24cd/DJVuKzGabIKKzFswID5LaaODRHrhoGw/rKImEU4AmLcAqtfHyp0bLytwuUqORCcSgBWgiUAGer4Rf5MWySP1yJ3/n9jnjZ3YTt/t1//29/jQ///V/UFi4KNM5mmNwesUMcShmk1GhfYXJDnZDHz5xHZ5XesXEGKT0EsjI1lhZrS4SKaLQ61OoNFpfbLC+t8+U/+R2iWFFvzH8ohRIMEscre/CNt29x++r7mCJHKUVrcYWTp09x+sQxlmLDidhx4XiT8kjmthZ59Osew0lKVFtERBM8JbHWMJ5OaS6uMJlMsSJF1Odf+n7+h19gYXmRa5s3MUZSqzUJ/PCoN21RuqQsM8owwBpLmlUDQUky49b4Kr7nUasFKCmRMjxqhaSVq4/+npOLu4d2iucLECXG5HdPZkpVfVYlFQdFju9XFMzCWJyTGJOBBCnbeD44l1MWBuWVpOmU0WjEeDjE5ilSaJyK2N0bzY0jbi9Tmhk6crzz2ndp1rqcXDlNoBVpkvDG62/xpR/+Egc3b6Kk5viph/BcHUqf5cXjeN6Qi1cvc7vfp9YWfPqzL9DsdDh2ZoPOxjL5NCXWAbGef5Enpca6Sla4NEd92qNnXQqF53loFNr57N/eIkmHdDpNevvbPPbIY3zxh3+QRnud3/j917DGR0mFoajspwKFLwMiGbC6uDw3jjRNKrNrrVlYb1eDeuMxszQjLwSLCy1q9YBkVuBpTZHnDPojlPYIAh+pqndWKQVO0W53MSYnmc0Iw7Dyqw0j8ntUwMfax6pKPMtYbS0jnUA4hxKKZqdGWRboIAQhME6QZDnNRgchMpykUiCVEql8akGM52k8LSnzlH7/kGk2JozqNFc/HjtF/GXxo7yP+7iP+7iPfxd/Ocwo7+M+7uM+7uPPxf0kfh/3cR/38ZcY95P4fdzHfdzHX2LcT+L3cR/3cR9/ifGJslMe+/GnndK6skNqfE+voRKUktSDGlmS4EpDGMekaYaSYUUDUqYaX53NKm63L+4yBqohEI2U6q7K2Zd/+f/5SErGo8+vO1yJUh7NRpOl5RbKg7AekxSObDjEWugdDOkPRmSJJQ5hZaWOK2DQm9FartMfTEgmMJtMCGOfWj2k223THxwi8EhmJRff2v3IOP6Df/ZLTilROcVrCS5DSkOg/WqazdhKg1sIjKymyU6GG0z3p9zq77J8poOOLMYoIgKcqMSslK543c45EBLj4D/93N/+yDhOn3vEmaMhH2MqbRp3RB8TUoJzlK78PjZOpSFzpwYQcDQoEYcxx9eXWVnt4pxhMknwdXR0U+/4rV//vz4yDqmk+z5Dagd+6LF+bonF4y12d/psXz8gHx2xBzTIusYaA5mDgmoILNTIAEpjMFk1oOWONDoArDDYofnIOLrLi66+EmMF6ECRjjLKWYktS4I4xElBd2WRhU6TssiZTVKidh3lecymM/JZgiktUvlEDZ8iM3h+TFlm+JGmvbTEsZMX2Nu8xe/+049ej1/5lc+6azfGbO300Tpib6dPa6XNU88+wNpig29/+xJX39/j5OkV2p0a/f4ApRULi22SdIqQBpMahr0xJ0+uMxiNmE4SwqBOljvKbIizislkxq/93zc+Mo4f/8XPuK2tbaIo5m/83M9y/NgprFUcHgx44/W3abUbKM+wuLSEJ6Cczeh2WtQbTfb2Djhx7BQLq00+2PyA3//Dl2m1GvT7O+zvH/LQQ4+SjRyTnR6+MPyTf/TR7+1nf+GXnBtlmChEWYf0vmd0bIzB95oYm2PKDETlxBT4MdY68qxACIVSEkeBlhHWSsAihKscfWyOdI64WeN3/8k/+Mg4PvfMw05KiXACoxRhs0bUDEjLjLhVZ+1Yl6Yf0z8YMklHOA8+89nPsb2zye29m7S7NU6dOMvzj/0Qy4un6DQX8VR89D45nKjokACN6B6aGXzCSTys+xS5A6FwplJC8zwPKSXOVS9aGIQ4bSiLAucqF+uyzAm0wlpLEAR4nkdezL5v8MaU33PpuZfRqbMF3cUYYxPabY+yyEFp+v0RpZV4MkAKiRcaanWFFwhGk4Rg6pMnQ7JsRL0IWGh3uXZwQFkKZtOChcUlDg5GWAudTgPn5qvUZSZHOMhNRi2sEnkgfKxTHMlsIfBReARIlBbUvZh+1icpUzKTYzKBNCF1vURpSg6HA/yGRfiOXJYYU0kGzIcBKlNkIarBKgGV05D5HtPyjiD/nUTu7n7me7Z0fihpNlskScn+/g7T2ZilxRWkkNU6z4OCO9pl4kiH2/pQ2AKNpdOI2deKIjBoT2KdxQsVzkpyk4Orki6VrhpCiUprQ9gjU1yHc6DvOQ0mqXVr+A3N9CCpigQtwIKxjigMUMKhPVhYWERrj9LmTEczMmtxFjwvQnoepizJ8hyhqYx9peXMI0ssdNbJB/MlcT/4YIvJVKOET1EYPC/A5AVRWCNstHjqyYdIpzlXLl+ju9BiPJ6xuLQIbkitERJ6IVmZYq3h2gdbFCalu1hnMp7RanUY5RMm0xkLS/Opn5PxAfU44Pix47z66msc7PeZTlMuXHiQ8+dP884776K0oeb7rJ08hYvbKGFYai8hS027EdHbr7wkn3vqcfrDIUEgaTYXqMVN7GxKaQyems+Uu/XmVYq9Me5oZsTIKnnf8cj1dK2i24oqMSsliPwYIaq8U5YG63KkKvB8jZKaokhR2iOOasymGdksIWzNl9t0hSGbTZHKQzdqmLzAFBpbltT8gMXWKmaSMT4YYbwcHUgOD/fo7e6RjjIGpSDpFhz09shNwu3diAunniYI6rijF8A597Epy59oEm+06qSJxZQCqS2eV1XjeZ4ThjFFWlCLYixQGHPE63VEUYx1Gb7vU5ZlpSzoqtDv/Llyqj765u+hYthohJXsK5rhKEeogFA1SIpqEqzT6lKvt2gsaYrcUBq4eeuQMh1h5DbOM/R7E2w+JU0KrFNo32M2yUmSglkywfOCewpxPdw+j+9ptBREviaQCl+qSgXQAcZW2h9KIqh8ItNJQTnL0MIRKEUgPRreIsfkCW7d2mL3/T3qS4qVM21MKCtrtns9Cy7A2qKq/gFBjnCGOxU2QiCE4o7oyJ3BqDvj63c3UmNoteoMBkN2dvaBEuUV9IeHBJ7PYvsewwsfeon9mofyPJwzyCJnwQvYOL7Kwc0BpSjxQo98lvEDjz8GxvLdV98iDxza97DCYqRBBwoRWBCCMjc4UW076h7GwEqDKD2aOqI3HVaG00VZFRsFjA/HYCwyLykmhrgVEcc+vpSstBcYBwW9QQ9rHL7vE8US5QniTlANcukRevF1zr5YmxvHaGwZjqYYo4mbPo12SBRJThxfRviaMJpx6tQyewcHWCuYjnN2tzeJa5rTZ9dYWlok9GPW1ta4cvkWeZGxvNJle2uPy5d2OHN2id29Q6Sev8kP+wN8P6Tf2+faBwPeeutdvvCFH2Kx1cT0Dti/eps48tnPA84vnwMn8MOcmhfSKy3S5QwP98lLOHfyQbZ395BaUeSH1Gp1AhFTDGdEav7mqoWHp0OE9nFFSYkjVH51spcSpXx83zuSohYICmaTKUpW9nklFc9dCks78kGA8RTNRpMwqHNoJswKqNXmm0KE0mdaJNVYfWHJZznW5Whf4RvBwVYPbSXd9hK9ZBeBYDmICTsdbmY5ufCYTRKC0OPqjTepRU1WV1bpeCto5YOIquntj4lPVk98OsKaqrrMJylBu0WSJERRTDKbIaxkOpviSYWnNVJVimNKKaI4IkmSu9U3HJkpHx2nquGfagrqXka4iws1stwjyyOMOkHp1/Ga6/iqRn9vwK3NHtbc+TI+SgpCfYIo8lhYfIJe/yrT/UvYtIcrC/w4xllLvzfF8zXt1jJaeaTpfPutyWSzevikZCAl0nNoXyClQiqNsxLKyvrNqiqZLvldSlkShTFa18BqjLMMpn1yf8ZDnz+DVZakzFClBmPJ7PwKWIkApKA0OQiDER5C+FXSgqoVcWednQOqpK3EnRcGsiwjjhucWN5g62CHeKFBXIuJ/RBDUWl8tVpz4xC5ujvEZWc5pSzQviLyW7T8iOfPb7AoHL/+Ry/jqRpLJ47z8PIivhRMbu/x3vYWx1Y6lK7goDcjGVnKRGALi1LenRqHQszfXOvNBp1GxKm1dU4vrR/9LBSpy1DOkOmMwWDC4faA6Swl7rbww4CwWaPZaVGS0c9jnDCYzJLNHK5QSARWZHywfZ1bvcY9vU+vfpCxuBgS+ACCqBZisyHX3nqdhx9/nOs727Q6Hp/7/BPs7UxYWe3y/lv7JOkQk0+4cW2AM47FpRqPPn4az4uR2uD5kv5hyWFvSlhr0x/MX49mfY3pdMLm5g4lCSc2Nuj3p7z28tuI7SGnJiWvfOdt/uB6n38W/Cqt5Rb/7d/7+yxceJSnvvSTDAY3+ZOvvsbm5g1+4AeP891X3qS71uG5Z54Fo8HAaH/A6HC+rvlgfEA5nqIadUKhUFpS2pxJUmCswZpqEC4Maijl025G/Mxf+1HCyOPKlYsIIXn00Ud48KEzBEGd/b0DFpbqLC9Vp9idnV2m45zSD+bGkfSnKO2xfPIEz3/60yDA5hmzyYwsSdnbHHP8zGl+7j/8Gwx6Y+J2g4Uo4rVv/QEX3/xNxjrD0zu8+da3ubV/nch3bG1fYm3lLAvdNV546q9+6JR7b3yiSdwPK9f4sjDUdJMszVBSoZVCyeoDWx2R7jiMh2GIMQXWVuPwVevFIZVHnlfJqUo2EmsdxpT3NGQwpSVNciZTGKfbFDrABTUQOZPhCFHmtOot8sxirUAKgedBvVNHB5r1zlMk7ZBb770C0ynOVcd1a0qS2ZQw9Krx4GL++L+oVZNbpTV4nqTEkJkCT0i0gVDXsTbHyhzjSrACTwh8J3GeRqoCRE5S5NS9BrktcZSAwyqDTUqsASnvsaubHGyBcCU4g/ICPD9AyqrivqPq9uHjXfU5U8knOFudbJSj7gX4SOpxTL3dJkkSyvGQTneRyXD+piaEvdvyKEtwyiGEJRWGnemYy9e2ifwGX/rUszQ7HfIsp384QGvJg+dP0WgFtBebbO3vkk4cs+EYReWgrr1qc5BK4O5hx6u1RjtNMhzi1zzatUU0PjXt06hFJGXJQiujJnfQwqO7vEStGzGcThgOJ1hjmExTZumMqKOImjF+pDEzRTLWqJkmLxz2HiJ1B4djfN+nVhekWYYpEpqRoygMymREgcKv1dFhSBS12do64JHHfaxpcWy9Q39aMB1l1GPFLJmSDRKEEvghxHVFknpIqUhn8zf5wWRMr9cnz3NWVposdBeJvDp+rjnRWODsZ+q8cKLO77+xxb/87lv0ZiWX3r/EwuIyG6c3GE+nNFqLPPRwm063zvnzp1k/fpLpIGM8GPKTf+WLbF2/xaA3P4kHxuArgfI98tEYl1Uj/8o5tBAIBXEYUq/XqMUNnn7qER44ucr5B0/ymRfPMRqOyDNDt67Z3Tng+qXLFNMuD589SbMZ88iFY+ztDXntvflCXM5BVGuweuwEb7z5LrMkwViBlhrfD7FSsz+Y8t3vvsGbb7xJ1GzwwpNPs7J2iucffoxvXb5Ivz8iu3wRL9DUPB+sYDSZ4Adj7pyHP24a/0STOKLSNtHCks0ql46iKKvqyzmkVoShjzWVJKu1rqpmjpS/oGq91Go1SpPcPcKrI1EsdaQBfq8k3qi1SJIJh71dmh2fIpuhXWWuOkj71AJohTVG2YQ0LxFKk0tB3Figu7TKzVu36KxsMNjdYrK/jbDl0Wkiq/QZnKUsIEvnVziFHaGEwjiLLSXCetSDDsIqlPVYilYZ9nuMZge0FlsoTyBLhy8FTnsIBNqver6erNFQkBWTqqKWOX4MlQr2/MehUY/J8gStQ9JshpXV5gJH5rRHa/p9/XDnMLYg8D3Wjx1HSclkOuWNS+9S67SQ2uNg/4BkPKGmXoxYGQAAIABJREFUChZbdbrd+a7qTtyVrDnyVQXtqgvdQZbxtdffZLg3w/d8zpw5wYnjK0yTIXme0q5HPHZqjf54RF2AMo5IeajA4fnVyS5NE/LcUJTzN1cpFbVGDecUcbBMFLYIPR88sAjqGAIRoI4p0jSlzDPcLCAoK7U632uyuLRAaXJ2ezvYwuKEpNbUmLLgcM/ixxmLjfntlOXlNjs7E1bXGhTYqs+8soAXwV7vBpNhyWK9ST0McDbloYcvoCmw+QTrBK1SUxYGk025ceMao3HBeFKyfrJNu9vAlPts3zpEiPmbfFQPONE8zu7uHudOnGd98RRnT5zD35vRHI+ZJpsEves8dTzi3YN1TPcYWZ6QlRlZmSClz9NPPY81Gd1jbU6eO8Hrf3aFf/4r/5Ib197n3//rP02r3qB5j/VoddcRckauqlOJ1OrIeetIGE0KPFGw1A154NwqLzx7nPff+gYPPXaMZqPNm6+/zmOPPYYpczyt+NxLn6XeUPQOd7CuhrWC0SRldA/7PoclLQp2Dga0jx3n9PpxanELqX2cVDhP0AlrIARhu4sfBPzR7/0xn3vpWTpBk5PdLpcmeyRpQbfVJfJ8Bv0JeR6ysX6BsiyxrkTKj5eeP9EkrpSgLHPAEUQeoJBaMZ2NMVbgewFpkeJrn9iPKfISkHh4mLIgDAK0dDibkZmMwAtRSiM4cjAXgBNHO9lHYzCcYgpXKer4AaVRpFmKK3PG/QGra22cTXHkmDtaEUaRJBmL7Tp7tzK0jAg6pzhxwTLtbdFtxwy0ZTwxBEFEmqSIe9iiaREeXTpmeNbDFw2OdY7jOc20P+Xqa1e4vbXNqbPH0aGHFSWFn5PaGVaE5HmJOdrcfCFoqxr9NKEUJdbL0aE6cp6fn8Sfe/Q8UpR4WnA4HDDNDAe9IaNpgnG28gt0IKRDCYezBuEcYehx4uQ6Dz3ySKUUNx5x7eL7dBsr1OIYW8bUNzaoB4J6rV55d85BPZZMxtUFq/IFUSjpdHwWWjGeJ2hsrDDJb7GzO2ByNaE33ee5R8/gSSjTHD/QdHSDm7vbSJMS6OoytkgLSmmIazGN5lH/fw6sKYk6azR0wdrqCQSVhnWrtYJ1IIuC0lniIKvc1oVGa0EU1lBNUW1AAjxfs9joVFocNc3UTIg6Kfl+TujHKDWbG8fyShtnfOI4pttu0F10nNhoEMQx40zglGQ2tQz2r3LqwnlqzTZKREhraDXrpEWltS6EpdsJufbBJsNhxmJ3CS8MKLIEnOLmra25cfT3+2xsbPDguQs8cf45wqjNN/7wZU5oyTMvPMHNGxfZ3N9GNhb4zGPn+dr7e6ytHmNpsYMzBZ3WIq1WkzhUJOWYr37tm/y9v/MPuHH1AFvOUEbQqsW4e7T9CiuxuaEsoRHUMUKgtCTNU4oyx1OG1cUaL77wGI8/cprz51a59K3fZ9zbImh2+de/8aucO75MqgJKV0NIxzSZsrLapt1pcP3GNvVmk4Xl+dK8UknQmgeee45TTz5OmuUU0xxjIc0LQuWo14LqXszTOCGZpikjKZkeDFGlhVIgnKKcWfqzMUJr1k8sEscxs2REaTIa9TZwb1ntTzSJSynQuvLIK4sCJSubJ88XOCvxvcqgOMlSfB0csU1AqYomZ7GEsU+RZzhZXWAKFFgorcEPArJsVvl3zsH+QZ/pTCBUjc7CMWRaUBQOTeXz6aSPkx7aD4lERBDUSCz0Rym9wZi8KNncGpG5mB//6b/O7/36P6XX36UoU6I4IM/LI+H6+RVOVuS4HJxLaYZtji2cxrc+Lkmp4/G13/8DGq0Wa8sdru4dsLa2xmGyzzQdUmsIVKrAlzhdEitDGMVM0hytcoS2aCfAVOI887BQE5xY6rDcrTHNFpkWcP3WHnuDCdNS8up71/Gkh5IlnirRQnPmxBnaC3WkrynyIc1uk9Wz59hY7VBXHpH2mM0Stg/22RyM2d0/ROiA/2pOHOfWfcoyJIja+E1DuxsRRR5xHON5HlEUsXG8wXCQkKcpoRSsLLQY9Q5J84Lr+1ssry3QbISsLyfM0hLhhXhRgLGWUZLQ7sTIe1xs1ttd1k+fZkGVLK8sMBqMUUWGV4zZH+V85oXHqTXbTJOc27e32d/tc/X6RZx0VcWOrMwOikoOV2kFuaMlG0jVYOonHA4O6WXzk1a9qUhTRZL26I9KnvnUY4ShJst8tnd6REGDen2BfjomNYr+7h55AsiAE+uKxW6TtUZMHEiOtwvOHm/y7sVbXL/ehyjksNdDCc36+sr8OHSDdJBRX2yyc30A5Gxd7XHqwiLdOnz1yhaHhWYBjyXtONWMacQtTq2tMtjfQ8s6t2/foMgmeGGNf/g//e+8994VtAJT5mxe22Q2mWDK+esxnU3wipxSS5JZgpMSowWlzZHCcXy9yc/85I/w0qefIvQdl9/+Li888TCxTSiHt3nhgRPk+zfZ6yfk/jK+59HsxBhTcnh4yO7uDlEjIsvms8rAobyA2sICuSnJypxSHF18u5Il7dPyBQOb4oSlcAX5dEJpJc74uGlGmRSgDKOi8gFtLLYBx97ebdaWxkxmQ8DSucclK3zSphBJUR0VrOU/+tlfpN7u8Ou//WsMpvtIJK601IMaSZEwHA4QAprNZmU15odYpZmkBoQiJESUskrgeY7wNEWWoZVCyvk9Ty9aQMkGnfoaQi5DOSBsL7K68TjrDz7P+oXzhL7C9xS+CpHKxx6p4CnheOxTP4R0YCzMDjZprj/L5df/GF+6/4+5N4uyLTnr/H4Re95nPifnvJl556lKNc+lKpVKogSSEIhBWqvdTWMj1sKmaQzGbtyN224/tA3Ldi9g4aYZTAPtRlrqhhZGQlBSaaDmQVW3blXdOe+U83Dmc/YYEX7YKdkvynzTUrzkU64VZ+/YX3zxxf/7/Sn5HklSmFcctCinrBrSCrAEOMM6nY0BC1NT9NsReZrwqY/+GOffe49XvvIqS0tHmAtdpsvH6ETb6LWY6YUJwiCkR5dGtUVdNFi5dZud3W2ax+oQKCxpIdk/aPW2Vnjw9MM06yXeefEldJZRcz1m5quM45Rn7nmaSmBRKXsEZY9cC15+5R38wKHd7TA1PYsdGIbdNZSUvHjuPXqxwtjwyKPvY94/wcROj/X1/Wuen/mZhymHFdIoY6LWIjOK3niI1hrHcYvNXJfBCGxh4wuH1597iVapyn33naLcmqE3GHLs0Azt4SrTcxP4nlf8r20T5SlxlpId8F42b66zc/scPcfGdU7SX1vlyuuvce7qOu2x5t275vnEj/8I9zz6JJPlw7QrFo6c4ebGDjpOkbaLxMFCMhoOcB2fNM+IkxjbcegN2ozHQ0a39s/EhZXgeDmT07OQJVz61nt4AtJhH5VrvnxuRL3uc/LuJWaPnaHmCmreDsv9mEtrDu8ur9EIHSZqAb21S2ysXKe/06Hs1tjp9ZAiJAwLL9T9xhf+7C9QucX2Vod/8qv/nHvvPMs/+Ac/wsvPfpnX2wmth3+Iz/3G/8kTJxOa5V0ePXuU1/6fv2T1lRe549hhtI5RacawH/HO7pDhbgcbjTQO0rOJxQbNWcHHjj2x7zw8TyJsjbHBkwYjFZoYnQ948gOP8Du/8y9I4ogX/+4bXL16GddoKt0ub156m8AVnF1o8dZrr+CE0zzxiccoNcqUKg1c16LeKFGuTPDHf/If+OPP/SWf3McnQAlJs7rH0h9G6CxDKgNCgcnoxjnzpTkuP/8yr33tm3gCRlu7vGALHj42waQnyXYSEIJ2lBF6Lll7g3Pn+4S1MnGsuXzxdUK/xH/7j39732cC3+sgnmbUajWMMUzVpmlMTPHh93+YL/z1f8S2bWqVQoKWjVOqYZkkS4hGYzzbQyaCqhfQaNUYjQd0ursoXQj6bWkhLJskzwpT4wNuBMKgivGaKCtEY1ObmGPm2Gmas2eQjocdBEi34Axj+WgEtsyQhYEnjmVjcgWWRaM1w4/95H/G8zWbb37lz0k2tvECH60K3vJ+Y7Y+g+eXKQcB3qCCimyyOMHkgtEoZvH4MY6dPsHW5iaf+9yf8+a5d/j0pz/F4tJxLr77FsudWzTmmpiKYS3voCs+V5Zvc27lAvfP3o3nekgjGcf7B62ZyQlaE5O8c+E8q9sxUw40wxDX8ohGPc4eWSIIYaezyfrGCvXGFBOtBs3JOsickiPxBPjSwSXjSL3GRj/B9j2q2ub8G2+hpM0o3b8WvTsa4YRhUXsWArTEQZKonCB0aNYbxYW3TkBrHGyqgUUgFSrtsNkZkRiBCcFSHkGlQtXzCsmmFMRJRsmxUGL/TV4DfRNTtgU33n2d3ZtbbLd7+EHA4VaNla0+5996g1I1ZGbpNP3BiFoJGrM2nXFEnuaErgShcYMCWeulEj2yUCZnPBwy6A/R8f5lnX5H0+9qrl+7zn1HWkTjDmfvnKQ07dPp5lwqRZQ9wWQICzWbjeurjHrXmJ05w7vbbfxgghSXlYFBmwa1SUFobjNsd6k4LufevcGZO44yMbG/9PPGyg2MsTBa8Mv/zc9QDUMmGk3OvfkCWaXE0sxRdGjTiRImmj6+7zJTk/g6o2JLnKBKEPgEXshLn/1PlD04dfwwnRFQirErFlVTpVLZX58thMFxHaQtiId9cpNgiPnwM+/n5//RTzPo7fL1r32dGzdv8cEPfpBvfuN5vvm1l5habHLX6SPglugLhzuOn6FUD2lMlNja7PDiS+f46Mc/zHNffYV//ydfZBAd0GciLexqqeiuiGPSUYTJFEG1TL3RQOYpduhz6sxJknHMcNAlnerQnJ3EdR1yFxzfQiC+Eyey1NBrDxkmA76Vfp08yulu9fadx7fH97bZx/cZDUd4vsfOThvXK3P62FmypyNOnjyBY7t8/vOfZzfZZjAeEZZCLGkx15ql6RWONrVamdOnT/KN119kt92m1+8yHA2J0hFGUDRDBPtfkKRJznZ3By3HGL2Ndny2xwknqTG7eJiaqzDkCF34AbqeT7Pi4VsCSwhAkuc5uZZcOHeRupuzsHSUyZlD7KxewHFcYpWTJvuXU1ZHq8jYoxI1mRgKAuOByvE9HyEbbG2u8/ZX3sPg0piYJdIWsZIcWjqJH3is3rpB2a3Qqk7xd89/i/WNTd659BbelOEeA6l2CdwKthPtOw+tFRcvvMfpoxOcnlokjvsIWyNcl6VT8yycmqHfb1PzJ1CBB0qyONEgjvs0QgvHByyoBFVaXs699x7n/PmrtHcjnF7MiVaT1LIYm/0/jmuba3TTMbVSBePaOMImR+GWQ/BsyBVxd4jKUuI4JY0zTh89jJunBOWQm/GYi5tXkIFksTbPOB2R5hH2Xr/BOOmDMOgD5qG0YmNjm3LVpXt9nfZOSj2wuG+pSqVeo1yaII+73Lr0Du1Ol7Wbt5mZtEkcuDloY3mSmkwwuaHXjwnKPmFYIhMp0WhMeTHAjAV5e/9MvLsTc+NGm42NlAePz1Gtl0mHEU1Pc2g2ZKoCQRAzVwa9/RZevkrsN5kP4cSMw+YoxUcRVpoMc49GybC7HWPXGyzOlrm4OWZra0ynvf8F/PzMHFpBqVSm5BflS4Hkl3/1v8NxPdpb60jfpT2KCWs1qtUyg84GvZ0tlq9qGlMtsjzh8OISE9WA1Z2Ie+46ya3tHq2lJsHkFLJahQNUVEkSMe52SYSmYgnSfMQnPvFhfvWf/dcsLE6xtnKLqekpPvThZ1DK8NWv/wbj9pgnP/4MUxMVFhYXufNjP0Gvazh6egkvsLh5Y4uzZ85gWzZ/8Pt/zO52ilPdvyaOY1ObnyEIAlav32A8GCIRzAQeSimG3QHawDM/9DTHzh5lZ3ObuWaL1tws3/j8n6BTi1xrsszGtzVpliK0QGoX4xh2trqMNnPirQOa4/bG9zSID3ujok5sBIO4x2g8ROuQJx96iunZFo7jMPtz03zxS1/klTdfp9lq8amf/BRz03OIseb68jXW11fIo5z/4u//DLnOGAwHXL1xha+9/DxxktBut8kPMBhNxhGuDLEcRcmRDPMx/c2rXDU2Ou6C02NuYYFBnBFlNkdPnERYFsNhj/FozMTkDG++fQ7HL3Pm2HEevfsIL73s8c65l9ldu4TKc9I0Rx9gi+baHuMkY629wezkIpay6XcGpAbcoMqwN2Q4gmrDx8pzStWQrzz7RXxb02o1CEp1Wq1pFg4dpnVzh8/+x//AdvcWP/LQR3AdF6Fh3OsRhvtnnvk44b13r3L6zh9A+SGzs0epNUI8CSXbcPH8WwQzRzCBRz5WuJbL4tQ8W6sXME5KajsE1QaNeovdrXW2d3Y5fmQJvSAQ2mKjN+Ty2gYX3rm07zxqzXm8Uoi2HbZGORVHUHV9XMeFzJD3R+hBcRFmZ4JOO8Gvl3A9nzAICVVOrVIjd0BYFoo9R3Vpk2YJw2hUuEodcGfywIN3UWnYyLJhXIvxxwOMVbSQxYMOk61ptFXDKdfJ8pxYpqyPRqQTNapBiWQcE6cJ0rWpt2rYwgYHSqWQsOpRn2mQY8hH+2+uzcmQ3ECadukMR7jlhF6UUM9D3CTn0YcXqNdchv0+q9dGtGYbtAcO0WBItVLGKreoeDY5BtPLEXFGpxMRIzj+6Pu4566M51+5ThLvf2I8PHOUNEmKlnWRI6WDwWJpqYHAsL58A6k0vTSjPjHJwuI8b7z8EvF4jO8J3JrHoN8mjYfIPCEZDVhcmmLuaIv7Hn2cCW8O7eVFt/A+IxntkI8GZFIT+zbvf/h+fv4zP0Wt4rF9a4VxmnPy9Cka1ZDbV68y7bjMPvY4jz36fkajHeaOH8MqN5iYkIQVH60VDz/8PqLRiGe/9BzbNxLyTEG+vxTWSjSBE7B75RY33jqPZVs4vsdop1OYQWvNI/fchZuNWSrZNI4tMDE1Q5bnRHFESaTMlR1WNmNEYGHZDloZVK4RCrJE077ZZ7j2fRjEhSnYGq7lsrazytT0PE7iMGgPCV2XRqvBodl5furv/RSPP/4kju0wHA5ZvnSdifokh5YOs3D4MHEyYvXGKnZg4fgOUxNT1CpVtrevYbQ+2LlFSjxbUKt61ByDO4rJBinJ1jVW1Zgf+5EHeOyp+3nvyk2u3tpm/eKbPHvhDeLeLkePHKfbH3Hl+jJn7rqXaHud/u2LOGFIozlJEITkeYpS5kCbp7nyDEM3JvckaRShjaIfJ0RRQqlis7azy4VrV5mYrDM9P420DYP+Fq+9/A0OHT5GEJTRCoTlElZKTEzXWThR4ckPvJ+g6SIsizRJqJT2v+E+dXgOZVzyRPM3z/01U1OTGJVw5uhhluanIdL4QZXROCLKcoSQhM0KU8k83aHk+sqA9d2r9IZvcsfJSdIkReWb+EEF3y/xjdfeYnlzk50D7MjqpTqWbSEsCyxJL0mIkgTfcvC0oCnKmNzi+u6YyvQR3rq1zQ+deYy19UtMJ+BZDtPNWVLXwcozUqFxpU1mdGGaCyhh4IDOwB9+/A6ywRa9fof24nHcoyUcP8C1LaLeLkna4ciZu5k9dgq/XMUbHeNG/xqh47KYKXSeI6zCbDooB6AKmzb2JJpZlmM5FvqAZKM+EVBvhCwtNKh4AXF3jc3eEMfWeDLi7keOc2hpnle++SIb6z1yp8zG1gZ1e4pLF9+hekjQPHmUNNqls7LGxvINhmOLzFcMdJnJekC9bDGwD+hgpfCvBcj0YM9rVHzHYeqdb71JzQtIc02eGyyl2O5sYcui9yGKI3r9XW7fvMXy9ogcuOfBe1g6Osf0zFGaVgtQBwZxrUBIG8eBJz/0CHVXcGP5XcIpB5NpGo0JymWfztYmq5cucvexJSqTU1TqVWYOz1CuV3H8AMux0SonixI2bq1y+dJVbly7Ra+fMEz7+MEBKpneiGuvfotLiSYjx1EWjm/j+T5JklCdbRGGAYONTYbvvknqldl+n8XU9ASNxizuzjXump6nv3uDAQJysDRoDCqFMLeoWTaW833osRmWbBrlErO1aQY7XSwUW1srjPoBShmktpC2wPJdjkwdRgtongyIopjlldu8c/Vtrly/TGfQYbu9SX80ZBSNitq1sAo3dmNQav8gPujtkJoQy0A7iXEdB3s0xgls6Eu+enGXF278FXEcE/g+jh9w7z0P4eqIZDxkstXk1NEFMgUbg5iXLq5jNLS7ApXXSbu7OI6FcPevAZ+qTjOOFWHY4Df/1R/RjxKCeg0pDbYtGPd7nDo5g23ZmHSIYyLuP13n8KwNXg/pG8bRCjdXe/zuv/0zHnrwDn76p34ML/AxmMImKrDwrP070LAVOxu36b+5zlMP3Y1lC1KlMU5I2prHPX4WS+fcMTfDTKtErV7l1vN/h8hikkGbe+85yiMNH8dYfPFLb/PGhWts9IbglnDcMukoQZkKob9/matR9UnShDRLkKJwQM8sB4RNOkrYlR47211eurHLUv0Y72QOzyyeYTTqU56tUld94uEmPTtFa4GIU/JsjLAtfN/HCE0Qhmiz/3v5vd/9v3AwHJ9rcPIDn6BUm8L3A5qTE5Q9CPJ1VNSjaqfMzB8i73m8tXEOLIt8r6PVpAptFE6a4tguKstIk8JD1pYSS4M5YJ2+9/Y2w36CJRXNmQp23uTKewPcfJeFluDajReoVQNaLY/6zCHqkzN86/ybDPKUq+c2UOomX/Nz5qZDJuoNhmPBiQcfoTLXIKjMsbXyNQ7PHuUv//bc/usDF7ARAlzbKZITIykCObz4wkvcXl2j7AdstTtsf+MWwrLY7Q0Ra6tcX9+gEw25crOPLgX84q/8Gj/6yZ9Dm7iQHasYy3IR7L9OK61jSGtEKmN+7Zd+hi//8e9x/YWvMjMV8L6HHi9s4myLdy9dZvv2BnfddZxHP/4R/FqDXFogBUIoJILzr7zBtYtXMNqi0pjm0tVVdtId8qwN6f6KkN/4wr9jYWkBy5I47R2oVZG1BhhDHMW4jo3jOMTLN8EtE0xO05hpkAqJG6VYl6/xzH1zPHq6gqUNbqWKMRY6idCeQ3tkkZ4pYea/D9kpnufwiY9/jA8/8iFuL+/QbncolQKMhk6/w/HDi/glFyfwsXJBqjO+/LUv896773B5+Tq257DT20YLTZrH2K6DsECjEHt1auBAAJZf8qgFkywcOkroumRZxsrtVaRTQgmPS5eXWVg4RK1exwtLBEGA42halQl8e5ogDFlZuYXjhYhQMYoVeTomChTlsiGOInJlQ3JA05F7BE+mlCtlRsNdVm5c58ypE9gO5CrFxadZK5ElIzY3VnBdOH60yfy0j+UGZNgYobm9fpOSJXjygcc4NnOC0WhUNEB5oDBk7B+03ry0xpEji0xONfmrr36Vu++7gzvvOIstXeJuh0qlRmtuCS0gUgOmy7OMZYP2zirCm+PO+57i4pVvcfvaCu1+ghPWcHNJbxDRa29hm4LhIrz9yzqZJbBLAY4oZIBuqUQYhHjCwUoM7Wtb+NUKD9xziCwIcKSgOdHk6JPvxxptkSYeJt0lSftI2yEoV8gzm+GwT789JFWa7vYO5YMu0CwX3/cYGoc8NxiVUQsDSqaDHA4Ydlao1GrYQmPUmIbvMxj2sG13T75YZNxaKYbZENctSJw4kjRJyHONhSDTB3T0akl7J8EPPGSgWV+5TrNV5ZH77sKJumxdvUqnO2DldszEksuiAr/Swm+VCMIhw+2UqekKtoKbt7aoz05QnXCxHIkv4PDRWS5cSRn19y/rAP9fQ5CxEd/+fRQQtOm5efB9hlnOS+feZbpsM0oMvVijBjHjJMWpVchLgqc/+kH+3j/8cYQ1QOUDtDEgXAwSc0A48lyLnAwpUnrb20z5JQKt6F67QWd6gYmlQ+BYSNemnyWcOHaWsF4lN2AUSASg2dre4ca1ZYKwxOTUIXa7KddvrGLZoHIb19nfc/T1V19laqaF7zq0b9/EVtOUA7+gbtqgjMbKc8LZKbzpJmMkUhd3gvNz89ySNUY9QaPuYMaaKErpjXJGo4ydbMz11RFb4xE9ZXj6wDfzva6JD0d87etfxww1D931AYb9EWMzxvc9cpVgWRau6yJEwRAZRRG//+//EIxGChvGoFAICxzbIc8Ugr3W8DTC83yEFBwgA6beqOB7NeamppmsVcj2alHCDrCcMh2nRqvZwnULZcOo3ycTERPleXq9Luurt+n0uqysbbDRSbCcAM+CbLgNptB/YhTyAJmMX53C1THajHniA3dz950NZuouSdxne7tLIgKMjHGl4p47Fjg010Cl61h+iu32EcRkekipGvOff+YHOfPAPOvJClpqMAXVMVM5udac3WceA2Vx+dY62rI4dfos3UHKKM45sTSNQWK5IX5tinanxzBy6I9drvRh+XafH//UT3B9I+O1tzeIB5pOkmKFIRXpohnRTwfoNMEIgcr3Px4mKBzL3usMNSAthO+C66E8w8hkGA15HuHXC2PZ8aBL6MRkacxONGAkBUG1gR+WSOIUkQrIUrrdHipXbHcH7A731wGfOTZPMh6SigDLK5OnMWoYE/U7ZOMOYaWO9EpIxyGNBvh+CYNDmmVIqfdMrAuRlFIp/WiILSXGMgVPzEiktBgfUBPXmaHZqtAbxEQj0Cbn6Ik5aq2QtJuwPii6d3Wm2M7W2eh3efCDj6MC8Orb7G70SZVDo1VhqFJAocY9dB4RmxzHqzIzB9PT7r7zgAxNXnRVS7nXhyH3KJuSdmfAys4QW0i6vducmK2AJdkc5uzqMYmGf/orn+Gzn/sin/zUDyK8IYmKQOaAhcDGkGBQwHc/rUWDbeLuBqfuOYpfrSCDCmUUo16XqxfeZjjuUp2dYXJxlgdrT7F09AgZNhILR1igFLevX+fChQu0pieZX1giSiXP/t1zXL+xioMgNwFG7x/EVy9dwdI/hC0kJgiwpY80EkvuMYWMhTQSGXoYS2CNU25dvYXlufizc/zN+pC3t3vcf6TOxfUdVrsjuqOMYabP+d1MAAAgAElEQVRIwwr9nQFOs050EC5jb3zPm31u3brFs51nmSwtMRj2KVVKpFmC6wf4gYswINBYluFb518jszMC3wVd7Po6z5FSkMV5AWfCYNkWtiw87i1poQ84pkoUthAk4whKHraw8D2fTEM07DKSOeNecUEqLYvN9VVuXT2PRIPKsEQBo9JCcOe9jyGlJhqNMVEHmxjPVWjhoOP9n8fa+F20SkiTEQ994H1Eozm6OzcxI5uJiQDc40SZg+85zLRKjPtrbO7uon0f7VVx/TKBHzJ3Rwu/MUmU9SH9/3HWhcbYEvuA16y1whaSG1cv8fFnnuDq9S3Wl1dJe1u0pibprfcZWh6H5g8z7kf8we/+Ec++8iJ+END5/BfY2thkMNzAtgK6wx79eESSC6RxCPwSUZahxcEnJOl7WK7zHT60QNAdD8nHQ4SRZCYnH8Wsr93A2trl+HyDtStvk3sppakyu3mCCEMcFzIpSEThNJ4aQVirc+XqVTKtId1/fdROnGW0ch0nlwy7O4zyjJXLG4R2xqnjC8xMzGP7ZXINcTQmSXKy7ghjgbBsbM9Bi4JEaRuN+DYm2S44ObZdOJ5Xw/2BYGtrXSZm6jSbFdbWu5RCn1vLayy/exnftdkdO+hxxOGFCrOHqxgBkcmwjYcKLOoLU1glidsqMV0qs357k/Vr1zhx+ijbK9eYXlqg1JAcO9rcdx5xukme57iuh8JFSheBi9ZgScPNG5fRtuDo2bM0XYeWp5g/epRB5vCnn/08jz7xKA88/iC//r//Dg8+cA8YjcBB4IKxMKiC502MbX93x3udj7BMzIc/+CitpUU+8JOf5tqL38TYCakN/V4HUw6ZWFhkeukwuZHkChwjIMu5feMaVy6+h1I5J8+cRVs2V95d5m+f+zrtdhfPdwmDOoG/P5q3t7PFc3/zLM2pSU4cXiTJBMl2h421VeIkRhoHx7KYa4bUG3Vq84f5lX/2P+P4NtNTs7zT7nEpGvPK7Q0iYVOenCCTmv5wi+MnDtPuX+LO992FcyBJvBjf25p4WGLY7hJFEZaEyakJMpNx5eYlarV6kYFh0CqjGw35yy/9BUpm5EKgjSbNMmzLIklzXDy0KYBXtm0jBUgJWmUH6sQD1yZPU86/fZ7rtiJVBjuoMjV7iH5/l7Hp7THKc4SUlOyUVsWnXqtiC4POYjKVkyrN9YvnSLOcMNAszbp86KmzRGqAygXpAZnWtfbbZGmCY9tYSIwGb3IR0UxwIoEctPC0wnJStru7bG/tMHvqfpxqDdsPCr63hty26YzGoAxSF5ua47gIAePxGA7QRc9NTbIw14R8yOrt20S9LrJUYm2o2NmJeOKTj3JrYxWB4N777uWVF16l4VaJDDz/8osYpfB9m1yNiNOETOckaY7RYGPh2AXMxxxwcTXII1ypkcIiVxo02I5LJgy5UkwvTJBvtJnSNWqT80RWnWrdpeb5SNvBL5fYHQ7J0whpJAmQuRajWJFphRKFTrvs759pnbz/afQdI26cf5X22jI6zen3O3T7faygysIJhzhPiQZ9XAWpEajEkOQZkOJ4FsIFaQk818f3fbQqTnvG9cl1hhqnJPn+u3yWGfqdjEynDAcx1XKNzu6oyHi7MYORhgSEFzJ/dJZ2Z5c0SxgkGfXWPFuDXXKZ4QQTXLt6i97uAMeJaLWadDoxDz89zXNffZ0k2f/o2u3eIk1jZqYPkRsHS5f2pLYGlUXk+ZDHnniU3/g//he61y/xe7/9W/zCf/9LvP3uTf7gTz/PB55+nME4RmlFozJJPy6Y+wWHR5OS7Bm77N9kLqTH0aNHefjhR1m9tc2po0vc/dEfxlgav+zjOjaW5yIsG7N3Qscoep026zevs7Gxhggcjhw6iuOWaXfHfOXZF7l0+SrS0SRZXFzaRvuHxcW5CdZu3GZ9dZtXvv4yruvjODaWMcwuzCFcj9B1cQYpxFNEbsDl195AWJJ3Add1ySyXcZLwqc/8fU7ec5qv/cWXefXr23vMHo1lFIcOL+07j2+P72kQj+OIcqXMyaOneN+97+Ol117gS1/5azqDNs1anR9/5icwSiA9xXZ7m5X1FaRrgBzbtVFakCYJOtdIJRG2xLUdBoM+jmNjVI5l2Xsc8u8+HrjrTi5fi7h86W1M3EUZSW1yjrnFw8TJmOUrl8jybK9tXlCvlFBpzm40oBJ6zE02cIMqCsFzX38eIWxi1+f0/FmeevgZSjUPlVG4AOwzNrodclW065ZKJXzLJ81yDALHr1AVVUQ2Yn1tjes3rnDk9GGiapWBY6OTBJHmlFyfqusQxxHK5LiOhc5z8niIMYbQD8nT/TcTIS20lCzfWuGJx54gOJTR7XSpOBWGw5Q//P0/w6rWcP3z/OAPfISnnvkIblCjl+e8c+ECq6urpGlKlmWkWYpSGlsUthYIRS4cjBCYA8BkcZqBtIuThHQwGtLUkAhFolN6roXddCk5LvV6k2RgqDYC4nFM1BmwOd5kPd1G2zlWplGeh8pScq0YxRFYFlgW0tp/U3P9ALvWoLpxg/7NK8TjMTqN0WnM22+eJ/A0jYkGtclpZhfrlEplHj/9OL1xj06vw8b2Bv3uADxIyopSSeNJi8B2ibUq6uZ5Rn6AV8ehhQbbOzFJmnDo0AxZGoGRZNqA45LrCNcPWW9HGNvB9mwajSqXl7fp7MCVK5vMzoW0+zeJBhk/8PQHmZuogoBG0+LKexGvv3oNL9g/iJcqVdzcRyEwKipAZVIjhM1bb73Bffc/ws/98n+J0YbP/u3fYFVrHDl2gjfevIrvORw/eoTnnn2Bs2fvJtIRuVRksiB/pklCJGPyPZDdfjmwF/o88NDdHD12hOVLl1hevk6zVafeqJJrC8dy0Fpgy4I8KoRhfXOVlRvXSUZ9StWQqYUlpqfmyBOb3Z0+75y/xHgc7dX4DUJqLHv/ZOPhhx5keb3Pxm6fjY02Ulg4lqDRrDHrOAz7AyLfY+bQJO1c037jbYJSCZWkRZyyLIQlUdKiMdUkUzmJKpRb2xvbGAmlqs/U7AH8/b3xPQ3iea44tHiET3/60/zir/4819euU5+uIn3BzuoaRmhGgzHX3r3An//tFxjFMbZRoDWpzHFwCiaz1lhSEsUJ0rZw92hfSuXkWYrr7X/L/UMf+jTXrvwRGyvX+b//3b/ljjN3cmN1lX/zB3/Atavv8ODZk/yP//x/wPVcBOA6Lh/5wY+ys7vL+z/+MX7+536W0C24008880n6owjHKJZmaqBy+usjDJpc7x88HatKteIzjiKEcTHCIU6G6DRia3eL19bfYuHQLFevL4Nr0wgtkqxHNsoKlojrkZKzOxhgLIkxNlrZBSozrNHtdlGkcEDn6BiPVy/eYmb+JP/bn32Fkw/ezaH5Bnq4S71c4u2XbnJops7C/CF+57d+k53dIXFuEWUR3X6PNE2/w4lxhMSV9ndoh2FYYuFQi+3dnQNpisPtISrUSJHg2gHjOAFhUa1V8aVDLx6SK0XTt7GTHWZLNWrumAtrawxHOWHNJ2mPsTxDaHkMxwm+ZWE5PqWGQ1Iqs7vbJsr2lzpOTzSwvRKj6TluqBjXUpRrPnOTFaRQRN0uJc/Cm5ohcCS2X+bYmafROseRAs+SqCwhGnV5/Y3n2draYqe/y3KyAVITVly0LXAr+58IJqdDsDWe3+Stt1ao13zKZYs0U4zHClsY4jzj0rWIz3/2Xe6/s8n7zub0VlO+8IW3qFbrjMcWadzn7B0t3r20whe/tMb2Tp8HHjmF7WRUmw3EcH+poxOUsVQGwkUQYIyNsDz+zR/+Pq9/61X+9e/8K37rt/81L7/8Jr/4T/4rFs8s8I3b53jp0nnKrRp//uyXOHfuAp/5hZ/lL956FmFsHL+ow+e5ojMeYCgy1GP3P/Rd5/Gf/vKPOeRJnKrNZPMeVJbvdXEWrlfWnkvYtz0G2lvrbNxaZqJZZ/LUcUr1Jpl0wdi0uwPePPcet26u4nsl0rQgG6q8QCHvN4Iw4P3vP8n5Kzf44JOP8957F9jY3eJHP/VjLC7M89Kv/yZ/+sKbuJ/6ONNz02ytbvLIh57ihS8/S1gqkQF5pinXQjrdPlubHQadEcbAzvo29z3yKNIO2Nr+PuzYHI8iLly6zD//l/8T27u7lGohURzjyQDb8fn13/1f2d3cZKezxThPSHWKUQbHC8jiDONYgClq4lmEMbpw85GFNx3SFN1P+f4ZXxQJTp04xYeeeoobyze5uXyLTOVYWtGq15idnuH1197Ati0sq8gM7733XuJozOHDR1hd26QeSrIk5af/4U/xzoXLzE00ePLRe6nUaiTDqIBz2fvX1gLbxkUg3QL2laUJ0rGxjUezVcWIMouHFxDaYpgMKVcqeI6F69cRwkdgIY3GcSzSXDOIhuS6R6kUIDOB4zgFxdDs/5GubbfxPZed7R7NyUluX7pCzTlBmva5ffMWjWbIE4/dw0SzxXDQZ3trl42NPrHJCvKxMghj0AKMkKRZjpDF3UaSCTxTYboa4vr769XVOGecjEnSHMd3idOEsFzB6mvIc/I4JU0SOsImLAXEuUW/EzJOMvrRmHrJJnB8MpGjNNiZJotTzJ6vq1euI3GI4v3LGJvb2yTZLolxyZQicGz8chnftVFJxPyRw7Rm5gjKdYwuDEmiaIAQElwXZQRYAVYl4JFHf5DxsEdnd5NRNGD5yiXevv4e7WSI2F8kQxwJ8tygI43KDYcPH2I42MZgobOYdKzpxwmW7XD7Rp8TsxNkoyHxICP0auzuDJDGolx2CAOJ6yvuf/w4g76k2484NNkiU0O02b9zNDUahUEYRayGSCmJ+wlvXnyTh55+lAubV/ibb77I3PwiYxdevfAmyzdWOHf+bYJqiZXODu974m6clsVOPMAyDsQFCsHzA3AcwiA4kHI5OTWBlwxQZIShXzx7kyOtopSKMaRJQhCGjIcjtre3mZ6dZ2JyGi8MMZaDLRzAYWX1KrdurxPH2V5t30Z9++L2gFp0vdnk2tVL7Gxu4WpNoxJQbSwx7nfYXtWIRkCmFX/3leeRtoUXuNx1x0lecmxyA5Ztk6cZea547cXX6XcHqGhEa3oKxy9TrtRx3JAD+GjfGd9biaHrkhvFansDx/HQEmzpkIxygiDg+be/iVaKNMsIgxJGaEphiZziIed7ZhG2FBijEbYg/7YTDMXFmeM5qAN40fVmnY9/7KP88Mc/hoCCaS7gAx94Em0MrmV9x6uzqNPDww8+gmVJhJQYpbBRSCH42Z8+iTZglAKdgkkwmSrmdcDtcpaN+bZgI4oiUjSpTrFzSUCdSqmEVhm+F5IZReCGJPEAaQt8p/CsTFSMLAWoFMqBi0Zi2xrfUdi2W5gEm/1f8+buDlXXo9WoETqKqVKD7ZurxDojikecXlzgGy++hhKCNAe7XmLa8xmNhqhxDKkBJGOtKJdKCGHwfRvfd5BW0aBhBEQHYAjSQUKap+RGkA362I5AZSkjYRE4Ab2tLpVShTiPUMMtaE5RTyyiHMZ5QkllSAqFUzoeQZSQm8LzUmWGUASETgX/gOi5tdVD2DaW5SErkySjNnaaIbXC9z20tEjSDD/P95RTBYrBdhxAkKUZSqdYloWWDpRbtPw6U2ScPHqWQxdPsNvrsHx7ed95DEYxcSRJsxGh47F2a5tcJXhuFdvRJPkIEKhcMdCKazfbhNYU43GHqfk6J5tzDHttDi/NMBrF5GpEY7JMa6qEshQXL2xz5s4a5dL+J4J20ieKYoIgwEiJbUvefO8CBIKluw/x7upNvGaI8AyXbl4mIWY8zsgHMZ/80U+iZ2xaixVkWdKsTxD4PkopNMXG3wh9hDB7mOrvPrIsRyBQ2uBKsByJMRZCFkz9PC3gekop2u1dkBZT84sI2ycVFrbtFaA9LXG9ELAKUxMFIJBCFmz/A4QRuTBIoRi3d3n+rQusXL1BqRri+B5eEBCGFaJoTH95iOW5NFs10vEAaTv0B0M8zy0UPrkmGY44dnieick65XIJ23FB2jiedyBt89vjexrE0zRCSot6rUK3O8B1C051mmRFgERg2w5CSFSuiJOIUuhRlLitQvtsCl9Nd4/Tm8dp8XLt4qcUksODRPJ5Ac4SAqM1ru+AFAVk3rKQRiD2+OTf/qu1QYhCyVFYlTnkyiBQCL1nWyZstAEtNEYcvBikFN9xKrJtG9sBVxt84UFiockYp2NikxPrnOF4hE2OxMZQALk8y0IYTSkIC/gXitGoh20ZAtshShPGB3QG1iol5iYnqAYBG2srTLem6Q4HyERRLpdJen2yKGMcJ9iOT932WFps0h/soJWmWZtgNErZ3Grjepo0TWi1mkzPTJEkMdeWVzHIA/XZw0hTLMnig/Olg8lBKU2mDXEmcLVkGPWwapK+lbLcXqWfRnRHXRzXYHRKbqC7u42KExzPJR8VqId+f4CU9oEem2mWI1TRYTl3xyO0r75B0ttCJTnG+MTdbVyRUAldyBtopRCy0IUboTHs1WSNweQKpQ3YFqVyhcWpSTInQGJ4cNTddx5RkjE9M8Vg2CaLFHEckeeK4aCPtA1IiTAaZQp/2e4gotsZMzlZZ7Xdpln3WTo0g5AWu1uCy5fW6fU0jr/DibPz7HauEsUOk1P7m3Vst3cRCFzfI5WKJIp54fU3ac7MMTIpuSs5fOYY3dttKpUGjhUz2u5y+tgd/KN//PNcGF5lbA3wfBdfF+YNBo2RgnEc43gOWudwgKH3uW+9zWMnDuM3A7I0o9vepdvvMDs3TbVaLgQOWrOxtsZoOGR6eg5hB2hhYyy3uOA3GoPEc32OHz9BrVpj2B8iZWEsobT+jiHKdxtZntNqTvDo4w3+au1LXH7nIsIo6hNNZpcWubj+HkI7WFKi0j6xldNp96lUq7QmbSr1EtVqhXq9weKxRWbnp6lWQxCCJM3Z3e2S5QqVfx9KDHWe4fkW4+EAYRRpnBMEPqHvoXNNlmV4vodruYWpgmbP6FSijcQJ/EJGqDTkMQqBJYqFjCoy8Vzl2Ac4YgyH20gNnu1gtMaybaQlMaJYqEJbe8wNged5sLcxCCH2AjgYvaeX1RnWXllHUzgLKV04cOsDNpMsKySMtm3j+z6u72AIkEqi8BimMcoY8ixHGrFniiExShQqAasI5spIpAjoj8coM8YYjYrGmNAnVimjbP/jcqNSQuuMbmfIUrnKeHsHiSYUhkqpjJUklL2QcQ6njp9AZ3nhaDMzx8Wby2xv3mSiNcFTD59Gk/DehQuUHUVAzqDbwXckgecz2Srv/zzIkIDOMqw8JVUxYVjGSEE0HpGohM6wi+8IKrUSg3yEGhtcz2M0HmBUhB/6JMMUC40V2MRpRJbmOI5Hnuiiv8A6KMMpmCuObVOdmKGzEjLcMqhUsdkfsLbTZenQJJb0KJfKeEEDYVWLd28Kd0RBYVP07TWjVF4gk6XF9OwC127cwg32l/ZJYROEhtZUi35njZl6k3anR6+T4XqSo0dmuba8Sh4XSpGw4nLtdg/XneD4ko0t+5TDgFxblCs1bKfLxXe7GCsmVRlHj03jeyX63f3LS7WJFkopMmHYHO3Qa4+4cOkqH/noM9SmpqgIi5mlNd555R1mpuYhEPQuvMOP/PAn0G7OKO9jHE2URoS2j9SiUDBlKUor0mFKrjLSLIaZ7z6P5770TTan3uOpj3+Q0ahNt7NLnmdYoiCgKp0zHo0YDYZMTkxQrTYwysaxncI2ShcOBCrXdHa22VxbwZbgezZpmpJnReJlDujoTdOcLMnIVMqJEyc4PztHZ2sHR7jEwxhX2GgNKokARWKnbG+1OX3HSRaPzFGfqlGplqnVatQa5b1kDoQo8NbNyRppkhFF34fslHKpUvg1GiiHFYTMUbnCaFNk2bZg0BuSZxnGCCYnWyidkuUKaUE0HiOFIKBQYFiWjeO54NiM4pQszTEYMvbPPFevv4zec2sPXA9LCFzPYzAaIm0bS/h7WX+RYUspyfKcPCtQtwbQSiMprNUKVkuOQaK0g5YGo3OM0tz9xAe+6zwqQYgvbSwjGKYR/d1dbMfBpUo0SElUiskNg26P/nhMezRmR0UkJgddtHUnacZwNEZYDuVameGwj8gE9bDEyYUWvrPX6LDPGA7G3NjdYnpqgt2sw2gc0ahVuPuOU8xPNTm2OElGQtYzDCPN9eE2a1eHlDOXhx98gHLZIYqGxOOInV7EmTNnmGw08C2be44vcW1jF8t2CML9a+LBtI+tCwbKINm737ALc+xxOsBv2NSqxVF8a7SFzlIaqY8rJMqM6aYWdV+AyVCjAUHg0x1HSMchz1MsA2kyQh8AwNpp7xD4fmH9JQ3JaIxtsj2UgsUdT/841967yMvffAU9HnLnYzWkX6czSov1Ioqym2VZe2tDICxJNxtw/fZtFhbmyOKI3a39m47uuPMknd4WSmumpyvEScLsfIN77pnigQeP8fXn3uDI8aO8ff42c3NzfOjDJ3jj1WvkaovZqSqODAmdkFurXV554zZRnHH8+CLNRgmhNctXttlYH1Cv7b+5nt+59R25rS88Ll2+TJy2ydWY27fW0DJj4cgUS3fM8C9+7Z/yCz/9Szz9wL1sbF3ltcsr9F2F6iagc/pe4d2KMNhuYV4tckGWxgdKUO9+4n4mcsMLr73KxXNv4zgurmtTKvl87v9l702DLL3OOs/fOedd735zqazKrF1VqiptRpsl2RjbeMWAwSzGhgZMR2OIgemJYaKJ6U8dzBcM080MTPfQtKehJwC3gWgwbeNNliwvsixbu2pR7Xvu213f9ZwzH87NVElYmSImwoaIeiKqKjPr3jef+77nPOdZ/s//+cu/pVqJ2L//AHffczdR2CIzrrdAGO0ggARYPFaX1/l//tMfc+LkS6wOhxCHWGERxhJkhlBu3fzUWe/RXV9nZX6FE8fPsOvwQZp7d1OkGZQar+H6Vny/TVBxM1jf9+Pvol4LUGGAH8ejGbUWpTaGjTsYtUKQDlPCaDRB6HXId9WId1Z7NJtNR+OaaQrthjjkeQ5WEwQ+kR9R4AqK3U6PMFRoo0FpV/TzffKsoKZCtIEi0xSlwxa7jkBBuU07c5F3HMTHgJIBofLJi5TFxWv4oc/0rlvAlmirkcpxsmibE3oSzxMURU6R9ZGe70JnTyHJkVKhDaPp8iWwdTg0zDOMZ/CEm4NZrVYIPIWPR+xJUmPR2nLo0F6slWjj8M4GyIuCQhcgLCjIdYotDKKcdi3rvmRsPKZIh4TbjKtTSrFrYoq6H+BJDxtXEUry0vGznJaKzxtBN80pypTcF/Q13Fvdw3WzxBeffBrlacfXYj3C0CMKPCIlaVWrTLQanDo/S1it4oUBH91CD89A5PlI4Z6h9ARCFQ6C2fAZDAYMhpok0wgUjUrM4vISgRoVoD0fXWYUWYLyBL1hjzQd4puQwPcJvBDpKUSw9SZNhqkbRydc26WstvGTLqQDwsjHtxkT09PkZo3cSnq9AUGsXUSIM1Bg0Nr1MVhHQ4/F4AlQaoYwCkkXtx6S8Za3vInzF8+gpIdvFpjZPc7S8iwz03u4/dgdpIMAS8nuPQdoNWtMz9ThgV3UaxKrC559+kXCYcith45y5Pa3cOLEVTxPsLx4jf17dpOka+w/2ML3tjYDad5HlxqtS3qpIckGtPeMMZQ9dKdwUUZZcui+QzQmGiS2y9fPX8WrQWB9ijRHSYlQ4HmWMApASawxlFpjS+Ga+eTWRvwH3vJmKsOM81cusH5tmdX1DusrXYRVHNp/lGEyZNeOfdSiMdCuj8QKA1JgpUQISdJPWZ5bobvSJelnpGmGNgatzSg/bhDbQIMFgjCKGN85wRt8jzLLKYwlzwuMdvaoEoeMTbZpT7YYHx+nMd4AYRFCopQHCIzRKN+lj4UAU+ZYXHpXa0HgbddJ6+S7asTXlteJfEdoleclYSTJhaAsIIoqCCM2PXVjDaYoKTHEtZi0zEEIx4+SF6RY9KjdPogq5GXhGPAQ7lDYQrS1SCNdg5DAsduVhiTNyMuUXn8FY8woDy6Jo5gsT5FKYbEjRILGlAWanECGlNpS5AXSU6PDQW2bm/fKviPb9zwmqlXWekMKIxEyRwpJGCkoNQ2VE/sBRZYz3mghkXg2xvcjSl1QmsI9SevjqQgjCqwsyYrEoVOKbcaA1WoIrYl8xVjU4PDuMSpRQFpauknOsyfOo/yQvQerxM2Q64uSo3GN+e4Kh3fsZ3yqjQwi+oOCx89e5frSIrXQQfzSXLPeSYi0pDaxdQGt7ocoBAiBtoYw8BC2dJvRunuqjSGMY4SQDLKUIs8olaQeVzB5ik4FeZ5QkDNMEowWULocbFypuAER22RTVpaXiSsVhHRET54MKKSP1gabavJ0gB/VKeIGiVYMMo2fDilzgxVq5EEJtHF5Ta01xlrCwKNRr9Lt9ul2e2T51hFjtRqiCw/he7zzne9kevc4X/nKIyRD+PJjJ/nQT7yP+aU1jh5LuHTpLJXY49DhSeqVmEFvyLf0RfxwJ0eP3UOlMcYtB+5gbW2F9c5h9szMsPKFy6Smx2CbaWTJsM9wOCTNUnSSImTB3ffcRhh7pNkQKRXNdpO8KBhvj2PKEl97xPWYShyiPEaIMreule85NJinCMOAQS8dDTvfWg+/WsEIRXPvXu55a0iuNUIYxifaNJt1FhaWmZqaQsYxQ4uLiJVLs1olKaViIATrukTUG+goAmmdHbAgtKYoLbJV31KPMIzwfI96s8H0zLQjMpNic6C4F0YoCWEc4oXO0/YC15sghMAa17yGBSHkZl1MSuVAEsK4fpdtDtcNEdsXAW/KTbkpN+Wm/GOV15d0uSk35abclJvyj1JuGvGbclNuyk35Jyw3jfhNuSk35ab8E5abRvym3JSbclP+Cct3FZ0COFb87crQI/mTP/wjyjLjZz/yi1TrTcyIhh7gd37r39Dr9fiN3/xXjMItJLAAACAASURBVO3YxXe47mv+kocff35UzTVY4a7oIKpi9C7XTeaaqTcuI151ydElxAbRv+MNEcIirbuORfC2B25/TT3+1W/8km21XJNIlhmajTGGwwHXZy+jtWasPeU4VcqMVqvu+D/CkOFgwDBJ8DwPrTXDwRBT2k2+7jiOUUrR6XXR1iCE5I8+/snX1OMP/uAPbBzHBEEwGnHlbZIICeH4yTcq6FprhHC8LBukV8Dm6zd+VpYOdiaEwGrQWpMkCR/91V9+TT3e/z/cZwUO8mi1wWqzeT3HMicxZYHQlkB66DLHryjwFEYJjLWYUiBKx+WiBSghiXwf3/dJhglhFCEE/Nnvf/019fjVj/yslaOGoFAptNYoKVGea7DKc4cHF8ohCzzPG5ExCbR2jV4aSVZqPOVRGkNe6lFHLdjCMVcWRcGf/PmfvqYev3l3zfYKTRZP8oaDAZdm1zBGsaMV8bbvv4973/GLDOaPM1y+gF+pk5Q9drb2kPYW0brPoICLiwu8eHWZ3/3keVZTg7CWWiwIPMn+HXX2TVW4sNjj8ednX1OPK1fP2pcuHGeQDEEXiKBA+gpFhMhjpib3kedD4kqI1gVSQqvpuNLTdEiaDl1jmoV6bYxur4uQliAIMMbgq4CdU/to1JujLqnvLB/96G9Ya0EpOVqjEqSbGgUCzxtNVdpgMR2ZBGsVdnRZC9iNhiw7mk00Wrcbz85aw8f/w2+/ph7fml23xlqUcoMgEKNegNEM1VgFBErgS4EvDb4SWOkjpUCMTIwCPAvaoSvR1sFSHcXvaC9ZSUttbyy/20YcY/SI4e7VqJgbcF/CQfieffopvvqlL/PC8y/yq//Tr3P7nXfR667z6Oe/yPXr1/n5X/oI7fFxjC42W2UdVOcf8rHsDbr8wwMTccPbb7zb3+kTvlp2754mLzKGwwFRFJHnjs94cmIXvh/geR55kWCtot4InYFVAVpnGBypk+/F1GoRWZLhjQyMH/hUKxUKk4LY6Dp9bdkw3kqpTYMNbA6X2DDkG0gmIcTmAbLBViiEg1htGHtw0C5jDEYCSuCbrSlgEQ5Tbeyoi9bzNw8CAF8KRBThgKQCYz2E5waDbHTSGiWQnnsuudUoCdLDsQHWfawpEds0UfhBsPm5K1GEMWaz6UuOqHKttaMD0lEnqDDYpFlQSqGtxI6m4WwcTBv3I4wizDZYZABfgW8Ua70uV69bllYtnaTk8sKQmZlLhN/6AnU5wJeCpBiQ6oKqnzPoJeRJB9+X7K4pxg61mX/rTh55bpm1vuH+O3dx5OAMt0zWqUfw7InjW+px+epZvvLNz9Mv+3QX1vFrlowMWQTU9CQP3PcuFhbm2btvmjTrEFVgrN3C9wPyPGF5eZFarUmeGmq1FmfOniGuBkxMTNDtdAhUneldq9x6+DamprYYCjE60I1h5GBYzAY2H4u1IyI88/KatMZ1TtsbdufG+rSOvWXTcN/49VaSjMirpJKoG4ZuCylQUhL4uFFz0kOMZnv6o65McFZG4eZ1O0Z13JAZlDPo2nVuar39UG/4HhjxTXlVd5YbG+A+pbEGJNz34P089sUv8/DffYEzL53mpz70Qc6fOctLx0/wH//8T5jatQttNgiIXr7S6/r1G1+8woE3m9+81lU21RavNNIbRtuOYo0b/npNUZ5EakujUaXIBVpDJa5SloZarYHyLEJWSbMuxuZUqjWSYYYfSDw/Jk0zSp1hrMH3BWHoUavHI2NqaTYqzkvZ5jTxPG+T7Gvz84yM2I1EYMArvn/1pJ6N12xSFIwMurXasTpusyDFxkA7KzHWIBSoQI2aSTQBjvCswE2S19ZgS7m5uT2lHCe0sXhKUVpNUbgRdaqUGOsiOd9sDRTXWnP9+nXCMOT2I0fIssxx23j+ZhOQBYqycBhwYwh832GgN0YMSh8/zRkMBvjK27w/nucRSEVSltv2EaRCUq1XmPALlnoJnQQKJL4QnDh5na88/xdU6lWsDOkNDP00o9UeYzjoIouSdsujWpGMhz4PHW6xp1mwup5z4NYZDt56N2blHGvdLvsaWz8XYwxWa1q1Ksn8gLoKKVZLJAoRQ5J2CUIoy5RKNSCMNMYW5LnGmIJWu4EUkomJcVZXeviBxQ8MadZB+RpsyZkzJ2m3JrY04jc6DC+vmdEWFq800q9cy6+8z0pseON2RI9sQY72trTbUjeneUEx4iPyvJedHiEEylNoLG7umEGXoKQk9hz9gqP4cJh4rS1aCka7g1y7X20tFKWmLAxE2zf8fJeNuGMb4xUP4caNr9kIS7RR/MK/+Cif+29/x9PfeoaLZy/zv3/sY/z2v/sYH/uD3x/Rq1oXzmxkaawaPTDDll61sZs6iI33um8Qo4NcGuvI729424isDmtHMyDtDQsHd/PFaBFYsT0Rl7EZYSSRUqCUIAgqGO2xtjLg6tWrHLvtVpK04xgbhXXMbNaFkkEQUqlU6HQ6BL5PFESbnrS1Ft934Zt6lXH+ThIEgfMmX8ML3/hz4883vr7RO99Mt0jHU6G1dmmU9TXSNCMItvbEx0SdvslJdeY86xEBkBSSyPPdRHRhQFqUc9kRWtKuVUlNTr9MMSMGy0go2iJG4BgOCw+EMiMqha1nfZ4/d44y7XP/9z9AZ20O4dcQxGjjuhM3aBiQEuUprAFbOlI1R4BRgjZUfA8bBxS6xBcb60YS+j7CGPJtmrBSWaPnT1PhGuiCqOIhbcZY1ScxBZ87WzDQGYIRB7aVILojm2QRVripWEbjqznesq/GbTsrXL/awXKcuoiYnNzHjmibAdY6weSWSm2M8TpU+zn3tsc4c/EsMuyTmnX6+iJlPyZdt3gWmtU67bFdjDfGKfOc5ZVlLl+9RBxXqdZjer0eaZpTr9eYnpng1PFZkmRrDhcjjCO4ExYrLEYYfAnNSkia5WS8bORdt62LntxwktFFRhvVWANCIjEYoUE7Wmft3Pwt9VhfnEVK6dJsnvP+lVQYYxgMBjz87SfYs3cfSVowSDPWOx0CCYPBwPEkxRFTO3bwpjc9SKVRZ2V9jUKXSOUi6MFgiLEGz/c50Dy0pS7wvfTEXyVilNdybIACT0pWFpZYWloiiiJKU1LqkoXZOZL+GlGtdkP6zHX5IQx5nnL69AvceedD2/zG7/CgRl755vO2rzhvRj97Ze5dvMJY29GYqdcXDSTpgCgMCMOYLMvI8wwloT3WACzJcEiW56RZQRCKze4/pXyGw3RkdP2RwR7lCHGHgtaWLMnxlCKKt+YsufFzvNrgC+G67QRuuvnGBnm1ty1GXZVSCob9Ic8//xxL83P4SjJ78TKVuMI9D7xpSx2yQYmIQXoeUijE6CAwwlEPD/sDtLB4viL0FIGSeGjKIqUah2TZAGuFy5Na0EYzXm0S6pCOTuiZFEqNv026baxe59Bth5ioRsyM13nxwiKeH1OUGoFrz/Z9D0ZRSWlLx34w2tjWuPysNiVWGLQtqcQVilyDFTz4wAM8/PDDbhTYFpJmGZcXz3PHpKQS+fTSgtD3aVUDfKFp+IJe7qghXC2mRBsfaw0lrnPUlyXSk4zHiv2tiFpYQ8gmyws9Dhwbox0HzHe2SSMKzTBP8LMhnV7Ou9/+dtrDVRZmr+PlhnEyhl5OJnI8FSK0INMJ650VTK7BlBRlTpIOGKYDmo0JlFIURUG/P6DTWX9Fau411bBmVJlye0xYgac8mvUYYTVl4ZImQjjv27FJ4tbPyxfB2W8xiv4N0hqMkBhhkMJ56FtJMeyCAN/3qcRVwiAgimNOnTzJX//N3+DnOb1ulz37byGOK4xN7OD67CzFMAfhI4IqhfB47IlvML1/D6vr61gBnbUVijxnmAxHHdshbzv8j9KIb/WgLEIoyiLlya9/nT/+j/+JH//wj3PuxfM88oWHmZiaYnlxiU9+4hP80kd/5Qaj4665srrIZz79KdJhdxsj/noy1lvLht3+/9PxaowdsSe6/J6SjP4IatUqRZFTljlZmmK0RxAE9Ic9ojBESc95GsJR+G7kjjcOEGMsQRhhtaXMt+GCuCENAtwQHjpagtH5imDEry7YLASJ0YBqZ8QFRZHylUe+yFe//GUmWg2mxsc5f+YEDz3wZirVralojRSUJkV6BiViUC8Xa6VS2FxjbIkQUOQZRsNkcwIFlElBXcWUwjAsMjfpyO1mTGHI0gw8p2O+HTVvNSYSgrXZeeJGzMnjJzh47E7iKMaTzlBYrd0gjM18udq8h57ncptZ6WgYHrjvIWZmZnjkkUd58ptPceT22zACvHDrUNn3Ff1CszbUHDkakesSMzDU3HAa3rjX49EzmgEuIiqsQEkPZUoCVTBWj6h5iql2hFAlZxdWWUxzGsOcLB8yNl5hMJjn2oXTvDZNG2gtyBLN0B9SjWMmWnVOPPM4p89ep2Jy6jWPyu4qy/0+QVFQloKpI/sIqCBQpGWCsW42wNz8PIN+Tq1ap9vtsGvXNIuLi5SFv63zo4TdZPxT7qxGWo0pcqwp8ZS/uVadf+zWqsS6DMlG0C6si9OtIfAlxgqyrMRY47z8LbWAc6eO0+8PGBtrc9ddt9NPNJ0VzeriLKsL19BpRqvd5thtt9FLcnbt3staP6U7yFCex87d+7jzzmM8/8KTnD53huXVFdIkYdhfByy9bo8gCEizFH7517bR5rtuxDfyEZvp/FHBwaVZBIK5Kxf50//y//Klh7/EsTtu40d+7Ef4xPInQBp2TI3zs7/wzxibmhrlwdhkGhQY/vSPP84wSfiFX/rFbfS4sRIpbvCu7aiabTeN1SveZS2bNdltnO3XY9ytdBN58t6AaljBaEuRZ6gopFats7Q0T2ESlLQUWYGwgiJLKYuCSlzBYkmThFyXeEqNyHUUWElZFAR+hLaG/nBrKlo1QmJspErs6J5aKzDWDXB298Kluxwnt3TezKh4JARgLA9/4XO8+K0naIc+dx09Rq1aZ35hgfbEOHqbglFQVUjpYYWhKLTzmnDeuK88ghAUCuVJhPVQVrG+3kdI8JQEa9GFwVeSvhkSNxoMhjndYUZWaqRyYa/0tpux6dNZX0VlAx795nkurwyIx3bwxjfex7XLlwnDEFMUjlQrCFxExMuHodZmVDKDH/nRH+WNDz1Ip9vl8Sef5PT5s/ze7/+fVKs1jh49sqUew8zQN4KzXc3bJ3fT7y6QyYRca6Jqi92TQw6uKa6vu7x8GITEgaEWxPiUjDcU7XpE1bMkQlDbeyvT++5gcW2WC2df5L8++gIPHapx/6GDW+oRBhWUjEnTnL3tnSxcvsiVq1dYXF2nmg5YPOFRkXsIa5BoQWZ9qn4dX8ZI36M7XGN5cYmZXTu58847sMZDKZ8zZ844bzaKyQblth6wP0IDOUSQQimJLyHwFFHgYbRbxy+n/0YOzYgh8OXU7WjzGmhWQ0oNoswxRo6mhW2thxQKYwTnz5zh3jfcQq3ZRqmQLE2467ZbeerEt0jNkNX1NXLhc/zCWfLSYEuN7yuGw3XStIcoNP1uBz/PaXg+tYMHGaQDTnXWEUXK2tz1LfXYkO++J25HqY9XYTmsNTz3zNP83sd+l+Ew5Vf+5b/kh97/w0hT0u33yYqUuBLRGmszNjblSgd2A/hn+eY3vk6aDPnwh36e3buPbqfE5ns3kYOvMswbBcptIryRS/6aZdAt32q0HdHaavIsR5dgjKBWVYR+TJqmIAvCMCA1JXmeu/SFMWhdjnLjAWVZYkyJEArfc0yP1lqkUhhrKYutPc8NZMmG8ZbSASvdPRAo5Y1CWLDW0Ouuc/r0GTzPo1ar0Wo1ydKUS1eu8tS3vsGudg1PCppjDZLUbc5ao0m9vjWxUClKkN6otgBCGOd1eRLfV2hK/BF1pzZuYk/hO+5v4/kYDcOupUmAlII8lZjIUgiJNpIwlCO+6K03qfUEZWGYmtrB1cdfoDE+gecr3v9jP8LXvvo1nn3mWWq1GkptkBfhDigB2hiCIGBm/37uuuce7rz7DaOfaxqNBs1mi8WlZa5eu+bYObcQP1DsbEoGiWVnu0Z/Z59+DYrSUquOcXp5yI6WwFcKUwjqsWBqrI62kKaa2w/tYGcrIO/1uLxmSP0mFxbWubjchWg/y53rnFmQ3Ht4ayrasgTfj0FlTI6NcfH8GZbW1shtQegLqv4E9a7B9yK67XGWhwWhiFnr9ukMujTHY6Z27WR9vYPyfQK/ghCK244dQyrF0oIbObedCCwYd7gLR5WGsCPvXIDduJ9CIOSI71+8vMVfjr7dv0pYPGkp8xKEwUeNRgxu7WwsLF6jMNAeq/L5z/0NU7sPEIUNbGEohgOiyCfPExauz2L8iKBVhzLDMyU6Kxl0Vqj4CpMXgGHP3t2sXp+l212nnw7Zu38PUioW5ua2vSfwXTfiBkTpLKN9ZR5OCsGR247xn//rnxF4oZuGIiyf+6v/zvNPPU2lUmVmZoZKtYqhQCLIipzP/91neeKxL1PmCf/2D/8IV9fa2lV2+TRXmXY5tBtyZv/A7IgV7nwX5hV1Tvd/2xiLbndAv2+oViLSPEfJACEVwlP004TV7jrNtpvOU603yZKURqOGtZZ+v7/pcfi+RxgG5HmJNgYPRnlySZ6XBOHWG2QTz71ZpJQozyMd9lhamOOFF15geWmZpeVlVlZWyLOM1bU1lw8uHYwujGOCOOLOW6aZmmozHAyYm7uM70cUxiCjCmqb9EGuQRclVkh0aYlDH4FECU2Rl2gr0MY9OyFcJBA2xCY6wROCWsWnOggphmMU/T6B8JENhfFyVGnxZUCxzSa1VuKNT/LYyQtUx1o8eMdBpm89wtSeaT7yy7/Ab078a/bv289bv/9Bmn6LI0dv460/+HYslnq9TrvdptQlRVGg8wyLoUgG5FlGaSyZMZQGlpZWttTj9lvqvGXvHvxoijOXz5IOQq6t5AxtTm/+Oj/zkx+kWWmy2vHxhy/hi5TnTl1hfm1A1Jwiax7mxOJ1djT38dTKWa6dOoHOC8o8591veZDf/PCv8Ngjf8m1C1tDDJPcIsMEnSa84Y7befjSac7lPqtBBdVbo1MM2FM/xowvCZVivSERnQG7lGFfI6Tsd8hyTbU9gfWaLCYJqW/I05Sxdosjt97BC89c2q6euEkxLbEgS4dOkorUWAZFTqmVM+CjoubGZn71Ptz4vrCW/qBEl4JBLqmpAflgjoLJLfV44rm/YZhm3HPXnejOMmuLZ1i40mH+6gpj42McOrqf1flVkrlZNIrOdYuP5tBE29FJ25zHH/kinhdz8eplvnn8afQIYOgmh0mSJMGqf4STfVwKZYNz+ZUWzwJxXMEYQ5IOCDwfJRWf+8znyfOcsBrRmBhD+T6gSNMOX/nSI3z2U3+L1Rnves87MYySuNvIBrxow4m2woxyunLTGlvpcmcbz3/Da9/04Dc/kUXYl7EwG9zRDq+0tS5RGJNmA/K8IJQBRaGpVGMQkmuz11nvdqm1xoiikMJoirKgKqNNalPJy/jjoijIstyNdLIj71lI58lug4venCU6eiaDQY9zZ89y+eJ5rly6wNWr18iLkrJ0GNpSlygpiOOIdDSUokiGxNZyfW6eQBiqYUA5HCIDQxC48VtimxNSGYOvPAywng6IAjUCExikkuSlwVjwpKAsStI0oVqtIYFA+UgDdq0g6ebMdQYEPrTjCp21dYKdHp6UlKXZdnNspIiO3HqYk+cv89mvPMFHDh2lyDL+6tOfIQxiFheXmV9Y5mc+9HPcfscdtCfGRsgU7ehxtePf1lpjgbws6CcDCp0hrKZaibjjtmNb6mFzyURjD4iYIowY3znOgp7j8pV5WrFPa+IA1049RyOKWFjtcmahw4tXB1TqbZpejOqu0B5vc2puhaXegKK0TO/cxUP3P8hPfuB9/IeP/Rbzc1d52+FtCt8yR3oZRsJXH3+Says9KlM7qQpLXqyhvAA3TLVCvVInkhpf+JT9IVlnjqKzQK1aoS0Deqs9drfH6YctOrog1TCYv8Iw6W9b2Lyx8G6txWiN50mEyRhvVbm20Ed5Lgp1G/WVr7/xOq6ZRqCtAlsitMELhgzLDoVubKnHvQ8cQQUeofRYGlowGYEt8YqcehyB1njWUvU9vCAkqEc04xCd5YSVOqISIaKYvFRMrlzl+tqCQxmZkk1KdQl6m3kEG/I9yInfiPX5+yKlJPADTFnwtUcf48knnmQwGFJtVKjVamCgs7rOI5/5a048/wyB0PzYhz7ID7zznaNc1+sw4pu64HLqRmIAJQRCutzbBgRJ2Bs6ETc7MTcstbuata4C7tCKZhQabu/Sr66tYa3GlB54zgMstWZ5ZZnzFy6QZgOMbeGHIVlSEEbRCK/sjVIoZhMN45wPM2p+2FjwDglrthk3dePmyLKMb33zCZ568pusrq3S6XYYpgW+FxL4EUIq0uGQ6amWG9RcWoxSVKKQwSBlLQRvcZmKJ6kEPmMTU0xP7aBaCf4eXvfVUqkKx8MO+H4Nz5NUAwWloNQwFlRIbeGqWqHBxiGUklB7qKEg62YMO9DtDhnmGUNVEk5Bs10bEUaXaKlHsNTXFiEcJ73vefTXlnjXe9/JD7///cxdm+UP/+8/xPcDjLGcPX+J1sQE7alJ8kF3s+GoLMvRoAFNWRZgBYH02Tk+TuBBNQ5p1hu8993v3FKP0m+QqjahClksAuYvzHF5bp5ce0w0pzh14hSzZ1+i2ppkNYelTFEdn6TIC5ZXV6jFU3R7KSfOnWWts8oP/MA7+cAPvZdkMOCRL3yBztWryGTIc2e3hlwaMyTJVvBsgy88+ih33nUnWb/PgSMhaZBQq7UIohBrNUWpgRzh95GUVOMQWURkg2X68yW9TkEj7VIRwMQEXZ0w6C5jENuujw2+7o2vo7jCjnbMeF1Sb1SZWxre8JqN1OCG0d64ih01ABlKBCUadIYkwyZgqGO8rZ2emX3j4AmybopBYK3Elg6Fc+HcaR48vIfZ/jxPPP5VPN9nbKrN3p27aNZbLuLWGptrhmnJRL2JzUoGwx5KuQ7xDaROmWZb6rEh32Uj7vJUDtzzGg/MglI+nbVV/urPPuGQGcbgKcX1i1f47X/zv3H50nWakWRiR5MP/PQHeNu7fwjURqv469Vl9KBxmHDfc40kpgy4OL8K1hCFPq1GBSEMvu9j9ShEG1W4uQEq/p3W33a6GG2QSrjpIkIT+BFFUbDeXeLa9evsmt5BtVpDSUVc8WlWaizOz+H5Hr7vu7mHRU5pSuQodw0bzTuKwWCAsSWv50AxxqCU4ptPfJ2vPPooS4sLDIYDtDFU6i03ls4URFHMMJGsd/tMjDWJqnWyYomycN657/suHyklSZpx7fosd999D1EUYbcZhBuPJnwLIfD8Et8Ydgc1KlhEaTl/YQU/8vBqEV7oo1NYubTE8qAgywVRXMeO5qxGUUhYqdIcq6GqGmu1wwWXnkvVbSFSCPzQx2r4zV//F7z1R9/PkWO3c+3yZSrVGmmaE4Qhc/MLfOpTf8uRI4dGB6hrFMrznFJrsjynPxhQpiU6KTi0bx//y6//GrNLazRqDeJtOmkvrw8JF+dAp5y4vMDaekqRZuzeNU7YmOKZk+eY3nUra8MOwzwlTVMGeUl/mLJ3Zi/tiR0cf/FZLl69xvvf8w7e/sB9fOOLn+PS+ZeQec6k7lNvgfC3vh9RRdEej8k7HpqCIPSZDsdZ6F+lvXuChhxH+ZJhdwnZL8jzPpN2glIXSKvxoiYrK5dZ7a6xtpzQXL5E0Fng+vhOeqHHznrTFbS3WaZFkbuNJhUNNHUrmWo1CERG1l8j9DVrvRIlFQIHG3y54exlp8tYi9AGQYEfRjRqEqoKPYxJsoJ+sbXTo8lQQhJXA6QXkSUWpULCMCSqeNRaDbIiY3blGtqUXLpuuFQdRwoPv1qnbw2iUiEMY5JsgC4TKp7voKo3RPsbgIPt5HuDTkHgkA6v/brz586xsrDoACNCMNZscf7MWdIkp15tUA9q7D90mO9740MIv4LGjtqxX08IIlzKBFfEC0PBaifnqdPzrC5c5+zZ0+Rpiq8k7bEmx44c4C1vuh9feZS63LTON7bo2pdLf6OjYXvDGXghoEnznCBW+KFPWRpWV3v0e0O66wmmjBBhgBAwSCxZqRhmGdVKRBwHeL4rCvtBhD+aKCSkZDDoY2xJGLoZhNveESFI05S/+/Tf0uv1KPOCarVCqS3VapXBoEMcBcQVn04HeknOdBSzY2Kc4TBhaXGFer1Os1ZjcnycShTgS4XvBygsUVzZdralLTVGuBZ638Ce5gR7/Bo2S2i123z+K6cpy5zY99nZbOEj6PdK1gcpqRF4uSCKfEptycsSFUYUumCjvVpKiOLwOx64N4qvFNIFYvzPv/E/Iqpj5Bam90zxoQ/9FCdOnKIwBVIbyizDlKUbL2Zx48YkKCTDXp/nnzvBrh1T9DsdtNUcO3aUu++tMeynfPazn9tSj4VuwjHlkRUw6K5TVwFBs0lUwPmrK9z3jh/i1gP7ee7JL5Mszbr0jZVM7pxhes8h/EiTIzlwy62848EHOfXUYxx//jgzzQqeTskLqMQRY9NTW+oRVCTUcooiZ+ZAFRl1EbpGJ1lnepehUTbo9xbQaULek6x1rxF5MYqcYrjqCr9lweLSMt2BYrA+h+mu0tl/F2m7RTFYJdB7kNsUN12DlYuCIxL2RIpb9o2N/s/QY4mFF+aR0sdTBVa6EYzGjjh4hAQ02lrqZUnD63KwPY1Oe/Q7yyR+namGz/L81k1HFo22Dqm1vDJgsDSkWYYkSQYqx1jNIBlAYfF9jyLPGTBECg9bGBaHQ8Z3zzCxYyedC0tMTLSIdzQ5ffHMZoQtpGS79v8N+a6jU14+EV+1oTdISARYmzM51qaUiltvOcCuqSbX5udYWV1lrN1iOFjmd/7bHxLXxjDW4aVHKF022qK31EFYuv0BC8tdLs936ZuIl576HGePs9Qf/gAAIABJREFUP43yIj7wzz7Eu9/3NrQ2eEJw+eI8iwtXGBpBM46p+BUqtaorrBiDsRY9igIEHlJswKW21qPf7TM21iJsBG7St4AwiigLQ5aWPP/caeZmVzh4cB979+0mjGoUeUyzNUFRJGTrKXmeUK3F5Dpzbd9CY8ohaZqyf99eev0u29F0aK154okn+PznP8+b7/8+1jo9kjQjCH1OnjzD4sISE5MtdFly5co1Qj9AG82FCxcY9LqEQcCemWnazQqesORpzu7d+/D9EDWaUyqDwBGDbSHlCGutjSYoPM6fXqMXSzxr6c/N0ytivLBBakuuz60RBT7SWIKoxkQYIEyJkB65sviNkr0Hx6CiGSZutJ7WMMgH23t8o07RIFD8Xx//BL/3f/x73vFD7+Bf/6+/QT1U/Nqv/HOeP/kS737HO/mJn/hxkmGPYb+HlArphwyShP/yZ5/kj//kT5m/OssvfPhneN+73kYQhXTXu3z7209z7txFhoOtjcWRPTt57sKAqFbhyJHbmF0bQFEiI0P32iwhA6Z27uSWg7ciiiHNiRls3iW268T5GWwWcHSsxoSvuPrSt7ntyBEOz4xx6aufZy0bYkPJ9x2rEFW3SUFWckTTJ/Ak1ZmMxfIcwnrEuwxlP+Pa489RmDV27p7ixMkLDJMV1te7xJ7EZimd7jqXVhMGRc577jvG2pzHueUu4UGDiCy5V+K9Ducr18KNfevMU58Jec8HfppOr8OVC9eIKgG9y5eoZOuI+gSFiEA7riZjwWx0WQsPaQ11tU5veY2TK+t4gaVS9Rmkc6wuLlEE+7bUY9BfJwgE1lSYvdqhN5vzzOx1JCW798TMfvtxDu7dy8zRh5i9tsS10ydIh9epxiEPPXAvpy8vcOrCFbJBH6kUR6f2cN9993F01x4+9dlPk/ka4+m/R2vxWvKPpmNzQzamhU/snCQrE95451GuXblIvzek00/Ye2A/w4HHk48/S3tynCPH7iCMvRHa5fXlUpSUrHYGfPbxU+w5sJ/pnRNU7n6AViXGV4JqPMbc1Q6FMWRlQZELZk+fYfrw7ZydvUbohxw8sM/xZUiXCze4mZzSNU2OPsvWCzPPc4bDhDgOqVSqjm5Al4RRSFyJ2bVzH3v37mP//n00Ww2EF7C8tMSg32d61wyeB4vzs+RZRiPyRwxyAuE5vLjWlji+sbP1O8s3vvENHn74i6TJEImls77KWrfH3n17HSba9lhaWkbgIYTFq/iIZMjMnt2jBqScqfExPFEQBx779h/gliNHqdVbnD3xIu3xcYe62SY3P8xSNuJJIwSJ1vTmlmjXKiR5QRgEVOKIPM+wyqfiB6TJAG0VaZljdU4UG4J6QPPoJDoosUKjfMcNo0uNkj7GbH0/cm1Q0rEQdjtdpLVMNht0lxbQacqD99zDB37yJxgfn6AoUgaDFKNzhPAQBq5dm+PLj32VpeUV7r/nDbQbVa5cuszMnt1obTh54qQr0G6DVx92u9TGJkmygsJ6WBFxeE+N/voct0y0eeGpbxAJhScU2WAN2apy284W0iiycoXp8QnefNddfOX5MySDOXr9RfKlC0x7Cc2WoIPHznGfxjZGPA520KjsRkY+RH2KMqUsS7KyQMQDTqycYtKWxDZnbVVjA8m1i+vEcYW0HLCUrLFe9VnoDTh0bYmi1yGzOUl6mTJZplHdiW+LbaF9VoCvct7y0FF27ohZOHeRxx95hCdfOE0wPknk5eyZ2UFcg9VEkAtLXmi00aO6lnXF+SJFjSvaO/YSSkVtvEKtFTJRGXL14ixnV9pb6uF5/qjeBItLy6i8SRRXCTzHGZNYSZqmHGk1+NDb38nylcv8+9/5LSqxz11v+D72HS1Y/dSnGdsxjTGGfr/PWLuN8I8QBY+QlD2EUIhtnJ5NfV7Xq75L4gy4U2n26lUa1RgRGbrpgD0HDvLh97yXd7/vvYSBzw8++Fbe9oM/wG//3u8SxWwiR17X7wGaccDevfvwggoecN+Db+K973kbjUDw7VOz9Loazw8ptYfVmqP3fj9hpUFrcvdmftgod7IHnqIW+2BLut11sty8rnxWu92mVqu4Crt0XoMfeIyNNZmZ2cl73/0TjLUnUZ6H7ytmF5a4cOEq1WrM5IQgzzXaBJuLM89zRKhGqBVJJa65qerp1h7f333m0whbsnvXBFEUE6DZOTnhcuzKdQ5iIAgjijwl8H2qYcA9d93F/NIyy2uXWF5bo+LD9IG9zEzvptUepzmxA2SAHygscltfK8tSwDEmalmSWsj6GUmSMMhShFCb/CzKU8RxDBgGWYHBgC2QgWTX3glSP3MDq4VwHZ15TuT5REGw6Wm/lmg9KmojKAZ93vXWN3Pbwb1cPX8WtKbdrOMJGCYDPCXJ8xRMSalzesMOn/7vn+Glk6eQ1jAz2eLqhXP41lCr1WiOtSh1SbVSJ0235k5ZWFxjRzXhtiOHuXzmWfI8Y23VsmtyChFJZhcv8OxTjzHWqKOLIXl3hb3f9xCV6jQvPvcXzkFo+3RLTW5CksLQbu2gFzVoTrQZT1e4vJhSrcLdWylSakJpqVYFubSI0KFZpJBo0aR3dIVdCazOznOpr0kihSiGiKKLaAviIy3G97bpPrPM8bOrxEUf1ao5qKAHuS3Qwjhk2BZSiXwaATQbUAsyTj7/dfrdBSaaIUZlRL7ApH1k1KGlHBVyb9BjaHLS3OD7Jft276HV3sf9D92JKATtSkx1vEHklVx55i+wVcO5+dUt9ShLTVGU+L5PvVEnGfp4AdxyaIZ+/xqL6z1OXb7MYuqza89+xiYn8KI2gzxh78EDnL1yicXFOTqDDN/3ecNdd3Hl6lVKBOPtSdbn1lFWvRqF/ZryPTTidjOFYq3jM1AyYHHuCo8+/AW+8dgjjLerLK50mF9e59d+/p/zUz//Szi+L8mhw4d58YUTPP3UM7ztHe/B2u25FzZEWIvVmslWjbjRZLh0jqcvf5t77nkDzYMHECLHWMOOccfx4HsSzzNILMOhIc2GhL7CCyLStODF48+yOn+VN73xft74xvt48cQpkjxHbMPRMTk5SRg6D3ow7DtjjiWKFUeP3cKdd96OtR7JMGV2bpbjx09yfXYeJaHT6dBqNgg8RZoO8LwAhCXPM8IRf/bK8hpRHI04VV5bsmzIvj272bt7hv0HbqG3ssCwMCyvrmCNJY5Cyjxj7+5d6LLAWMtYo4YQsL62Tqk1K+trmEpAs9lCG6hUa64ANT5JWbhDxGxzyvp+sIlX930g9ljtdyiEpKBAF5KcEjAYKehkCUlpSEtD1YvAGqiGFIFxlLPaQwsLynWUekJhdIGUW+thhMWWBVJAHDd54I33I63ClIKJHVOE1RgvUGA1/V6PIstI04z+IOXrjz/JJz/5l2S9Pnt37GBudp6xVpPl5XkmdkwwNjnuEAhGk5dbo0KkHZJ2FsHkDPKS9e6AtoJ+4rHWE9y+5yDffO44naWciYlJilzy4jNPcP+bf5jxsaN00yU+87lHOXnuAm+991Z66z2mbzmKvXOMXv8KZW+NxUspSTrgJ7d6Lp4mjkqEyrDaom2BEKBUgLIeR999J+rp8+iVgukf3A8HalRkDeVLvJpHGRTkaMKozZVrLzFeryClwCNEjtcwWqOGAmG3dnwO75jAK/ucfPoZdsUFRlgqVY/asKAeC3LrEZoS3bmCxZInOaowRGjMwDK1M2bKrzLTrmO7pymSNZaXM+YvO2rYhRef4srlhPmV6pZ6OKfIkGeWyakxrq0OKbOMYTogSXOyUhPGVfIs5dy5sxw6eAv3vemtfPsbD/Px//zHzC4vUKlV2b1nL0VZIqRkdm6OTj8hCkMiT4HwqTa2hjpuyPfQiN8AAxpxXPzFn3+cb3ztq3TW1qjFEePjbR579Cn6XYelxhZYJNaW/PSHf4bf+7f/ji8+/Chve8d7/kG/2RjD0Hqoesj5p77AxROP02y0OP7tL/Phj/wqe6f3k2QFNuuRYUiNS/HIUbEhGQy4NHedA7fcwje/9mWeffKrjLWaPP/UE3TWf5Zjtx5mdu4KjbGtC0a1Wo2iyCiKkizLiKI6fqhAGWr1JlJZ1tfWGQxSjr/4IvOL84xPjnPt6lX6l/pMjI8RRRHCasIgpNly5EfGaKI4ptvtk2bFtlHBwQMH2Ld/P9O7dpGkGVL5NEKFEZIwrnHy5EtU4xCTJ4yPj1MUJbtmdtPtDUiznMD38ZRAlwVBpcaOXdOukGlfhu3fyDP+ms/Flg5eakEITa2m8IMCdECr1Wbh+ipCK6xQJHlBf61PVua0ogApBF7LY3x3E02GNI4Z0kpXI1FKYaXCakfWtZVIa/Clh81zVtbW8JTHnpnd1KstsBpdlmRJQhCFDHo9lhcWGQwzzl+4zKOPPEq9VmXn+BjCWpKsYHV9nSiUeL5ibmFhFMltz9Ex2YpYXp/npdMnyLVPXmTE0ThppsnThPGxgxw6rFlcm6cRKi6trdKr+XTWZgniOlfOfguKlDsOT3Px1CkqEYiyiyDi2rWLBLmCRGO2Dghe7jfwFLEK0Kag1DlFkRF6IaKtGFY1w37K9KEZ/Dva5EmBMSXWGCgsttRU2xq/aWhXqyQLA7xhiR9aSiQqk65PYwsZr1doBBVWhjs49eIzVAMJ0mDLnDTJkZUAqztU/JK8KMlyj3ojxuQpdS+iYX3mT52guPgC8tZpt2aNRoZVzl5ZIkj6XLve4fLy1jQVQshNCO/4jiqXX1ogN13Weoo8yRw0tlIjT4acfukkO3fu5OLsHFp6fO3xJ8lMQVRvos1lR007ohIoihJdpigy1pbXqUdbcw1tyPfMiFtrRnwTHoPuKo8++kWeffpJAg88BSsrqwjh0+v1mJmZ5umnn+LHfuaDVGtNDIYP/tyHufeB+wmjiNdFZnKDaOHQMePVkGcvn+At3/9mmmNjLM0v8tm//Wt+9IM/R3dtFcpiRNDuiJewgrLUeL6HTntcu3Se408/zt1vuJudO3ewMD/HI1/6Eg/dfy9jjQbFNlC21ZVlgsDH8xSVuEIYRnhSYaUlDnyuX7vKykqXLC9J8gSLK3b8f+y9aYxl6Xnf93uXs9691q6upau7Z19IDjkkJUoiqYWy1thWIsFJoAT5IBiJg3hBPiSGYQMJ4CwfYhgwEMdbDMcGbMmKZVk7JYuiGJkSl+HMkLP03l3VtXbduvvZ3iUfzu0eUtJUjWFgJAH9AA10V1XjvnXvOc953uf9P7+/kJIgDOuhmOGQsshJ0h5xowFVTuYNznlGo5I0bRLHZ6tTnn76abYuX2H9wgo//c/+H7qNiMsXtyn2D5kWU7rdNt00pt1ssLi0RNJoknYWOTncI4xCWkqz0G2x1G5y5clnWFy9iBdqfkYgH/Ujz9soFUXNoE6SBINECEdrKWF0kiNiTyjB+IxpWVD4irCrKUYCj0YEkqXLHVqLER5NZe0cqG/mglZPYWrI0Xl9N42klaS4yjCtCh6c9un2esSRxpQZ3W6XIAgoy5Isz7l//z53bt+lPxiRJiErqoer6sGNWTbDVCWbm5tsbGxw4/bd2uFHqpqEeFY4hfE5N27f4rknPsAzm21GxhJZTbcdIXvLfGDtGSb5kPzgbY4mbzIpDfs7N2m2Eo7v3+Xk2HKQ51xqShKluPfG62QzixQeq+uWUSs8u41RY4pDlBLvjLlLVZtuCE+QxujVHsfa46YlMTFIiXFQ2QosCCdopDHeWSY+wOcQTg0iK5m4itjac+/gz7/2VVqxph1IWLvEpJgQVDNUEFN4gReaZjPgySdWOby3x/DmIVVxihOOsrCYbIQWksnQcnt8Fykt0he0uj3GxwVuNKB/UDArztm55sX8OnK0OjFezTCMmUwFV7efYn/3PtIKQi8YHB8z6A8YTMZcfvJJjvdjjk5OaDS6rG9skGUZ2WzG9qVL6CSi/4V73Lz2Fr5S9PePz3lH6vgjYqd47tx9jX/9sz/HK195hUBqDg+PeOLqZZZXFvj0p7+DJ558ii/+7hv8zm9/ke2tFX7qp36SNA1w3iCFgjDgyWefqSec5qOX71kj7j3XXvsyUaB5+aMvsbZ5mThJiYIEYwz7d28TJA3iRo9Qx4+elkLUMidnHaqRMerv8dQTz3LpylXSZpPBcMTiInz11a8xzS2tpbNPuWfTEWsXrqK15uDggHJWopOE0AdMHoy49ubnsUiyoiCvCpotzfHRKc5WNDsd8jxnODoF77m7YyjMjG5Ps9Br1P16ZTk5PUSe0075jk99DycPHvDP/8n/TbuRMMlLrI4oK8doMGBpocOHP/AiFy6sEzfaLCxfIG13+dxnf5nhV19hMpsxOT3mwkdeYvHiFjMDvshqxZB+uAvwda/5jOikDR7y5yrjKH1JuhQhlUSWjsXtmOZ6gk8tWIHygr37BQdvHZMutiGFvePjul0h559V5ZG+hoNZI5BCnT/eLWGYTVFC4pVgOJ3w1Ve/xvraCk89eZXj0SnDMiNtNnjzzTf4/G9+jslgyHQ2Q6gAJTWmzAm0pttI+PSnP8Vzzz/L6WDEnTt30EGIw2LO6c0/t9mmfagY7ozZGRxyaWmDRtrCG8+rr36J3Qe/zNbTH+QHvu9P0U8Drh8N+cLrb9IMNauLMVKHXDs8JFCek8xzd78GTSQS5NxcYilS2HN6r845vFNIYmKlcd5gRUUUe7STDMqKdKnHpctLPOgPaboAGUp03HjnrMoaVOQ5Xj9k/cNXUddPGJ+cstHeYHmpQXGrce5ObTpz5LllGggWmwu0Vi4QqLmBghOcZnBsDW9ddyi5jdzYYmupS1XMELOCxE9IAzg86XNwekokQ5Y7i7Taq1zdWiJXIUsiZcOePcGaZSVaBRgLNpjwHT/0BNPxjPEg52h/F6MFaRRiTUU2OOX2m6/w9FPbFLMxcatHdTRgMBjjxC2McTz/3At89tc/S16eIKMRP/GTn2AwKNnbHZ/9wczjfU7iFqks2bTP//E3/1eKosIZS9BM2bq0wQ/96I/ykY9+iE47Is8r/ssPfpK//b/9LZQM6HQXv2VISD4c4hbfqkmpJYZnr+LWzbeZ5gW7O7tc3lji1a9+lW//xCf53Od+m+7yAuLgAGdKjK0wzuG9Qsha8+m8w1mPKQvCKMXajK9++Xf57s/8KQ6OHhBGIZtbl/j6N97k5rVX4IfO5po7Z6ketkZFvfUPdIhWiuM3buO8JE5TbFlS5hVVPsWbktlkhDElVT5DBwFVCc42cEZiSknhHHGUYKra5/GsmGUlb772Cu1IsnZxnQrFaf+Uk+NDTvsnbKTreBRRo0NvcRmpQ4o8QypFGEVcWlzgmSe22Fy/iPe1442cb46EBbzDz6cgz4qajWHx3j06i6hKSVlO0aWje7kNicGp+kNXXrHYi6mWYtJWQBBIdJLUNEhrUEpglQHnkQrKyhBKRcDZqhDrasSu8BZhQMt6CnNnf4+002Y7aTAtJsRBxOHuAW994026C0vkpcO6HGMtkQoQQpHnJffu7WK94/7+IUVlkEFQExrd2WCypdVFkmYDrfr8wq0+r1/f4/s/9hIff+F5Tk8O+K0vf4Xbh8c8ONlnNu5z7c59Bv0ZO4dH9HoX0TLghZWEiw2HzwuOshAnBVeWJbPMYK0mdI5pdZ5aZ8akPAHZxYoAIU3NLxcarSMaIkAElt2hwcoZQZXQbNRsm6p6B/GKK/jg9/4IWfAN1pqXmN2/SCUESRBTyfPBU0iBE1AKx6T02ElFHDycvK6Jm8YrSqeQtj77+sbepB6rNzMWY0VLCmySEOHxtqKvFaBIrOGkP+Z4vMfdk7OFAIGOeJh4dAg+tMRKQBTgoybVrCBIQsqZJ89zvv61V9i6/CyD0wFlVbKwsIDSgpW1RbrdHlVleevNN9DxlE995gWSboZIU1pLnbPfj3m8z0m84NobX+UXf+5fsruzB9Rmoy+88Azf+/0/wEe/7VPgDdaWpO2akfLx7/xufulXf43P/Onr/PDl58DVB1v+36N98vsjCSOWFtrEyoAUXH1ym1/91V/gUz/wo/zcT/8TLn7mRUxZEnqHcwYlarNfAGtKjHfEae2bd/fm2/zwj/wIv/ILP8+3f+r7+fyv/Txvff1N8unkXO1ro9GkLCuktERRVCsvnEcrQRAGpIniQX+IMflc6wzj4RDhFUU2pqpKnM1RYY2LbaYNFns9Ql0PALUaKcHcp/KsmM1mHO3e4dlL69DpkXYWoZhwerSHkpKVC2usX7rKxqWrj7SrxpSkjcaj/vDSQo9WMwVXEUiJe9jznXMsrDHn0hSHkwzwtYmujlGyopqG9eRdOkcIVG4+yg5WWLSQWO/xwTy5UHNWQq3x3mNMPVzmvEdoj7Ke8Jxrp6oqpJZUVUkax/VruYqitLz++jdIopTuYo+7N+/wld/7Uv19A1IECOkIVK2CwXqCIOTW7ds8OD0habSIG00Q9VTpeSwZX2ZobXn6Uoe90TG/eWvEz/7GF7h9cMD3fOh5rl97lYPTE1599RW8KTkelYyN59aDY66Ui2S5o5MEtJue3toal1zG0emYbjskUDnb7ZiqmHE6OZti6DE4X2BMjg4iytJgbE6oYkSYEKqYoKVpbK8wnWb0R3uEUZuAoE7gvh6Fkybk8rMfZ3dwRCqXWVxps6OuY6WaD2Sdv5V+yLJ3Xs4tzepqQTwcthP1uZXxAiFChBY4X+G9p1+WDLOMTiRpJ1H9f4Slyoe4fMjuvUP2Jp5TumeuwZhqjlewJGlUI5utxjtBux0xaBuwhjRt0n8wJNSa3bt3EKJmHDlrcViarZCD/fuMRmNm0xEbF1roSCOQNJIUF/4xnNj8xZ/7p/zyL/4bBicjtq5eZWtrm099+nt49tnnaLZbeB/UYvwgqicgveOv/k9/g8/84A/w7IvPz2l1ct4Cf8/z9X8g1nohG0tPglB87jd/m6PDAUvrT/Brv/zzfOyDT/OhSzFCxPNBAY+SEuNdPWbvajZxZQxg+cd/9/M00garF6/yyu9+gf/iJ36YZrdFGC3VGvIzQog6gTYajUfKmqqqEL5Ogpsbq3TaLcaTGabIiZKQvWJCkrTxrkIJSxQI4hCUFqRxxGJvgarIybIZYRghpWAymZy5jgfHh0xnE+LmFVoXNkjbXaaDExYWFtm4sEp7/Soraxv15J21KKVI0wZaabx3lEVGYWpuxu7tG6xdeoKHZxTWWqwpH3lRnhVRED5Cz+IUzlV4PDrWRHFd1da93Bp5YL1FW4EQiqpyWOPq6tbXXpZKSpStvQ2tB5AoL7DF2ZWWkrXXZ1WVmCB6BEsCGI+GSBx5NuMbr7zC4eEhURSiEYRK1bZaOiBOYoyrcQ1RHJE0UnQYYqwn0SFSajKTnbmORpQQVBmTynG5W6GeTHjrBN6+fpv94xNeXFum2WgyHk8JlSZqaq5PK944nLF4bZ+VVsRCukDUadJuVoSze5S54I27M9Y6CXujGdf3PJPi7B2S9gHSCJwsCeOUUEVMZ32yWY6WlsCCEiGbl7e599ZNZuMxp+EYXdVm0mEQUM4c0mo2n6k4OilgUbO22GRwOqPl6/bF+dRnP3ekFxhX99yFUPX0tZdUDqwHj6wpl48AtBJ0hEWjgggXemRcIn1FGEiiUCJdRaM5o6MkRXWOeYnzSCWIYo1Skjwv0EFN/gy0JG0LXOEZn4zrVCVD7u/cpdNpMxqPwXuEFpyM9utdKh4dSJZWO3hpcBakNzUq4j3E+5rE/8Hf+/tEccrTT73Af/Vf/wW2NreRMpzfJLV0sI457VDA5qUNNi9tAabuh/+hBMR3kFbvRWW4e/AAUxWYsmD1Qo/xZEJTWhY/9AS9XoeDo+MajPWw107tJiLmr13f4rUp71//6/8js9mMMAhovbBGq9dhsdurVTTnVOJBECBEDZ0KgrpiLsuKPKuIo5AkCUiTHqvLSyz1FsiKGUcHh3jvCZMYQYTSDZqtBpWtUKomG0ohkB5sVZHl2bnqlDu3b5CmCUmrS7O7iFCapNVBKk2RTVi7uA5SU5RF7SMJGKvQShFqydrqChcubjE+PaHIh3RX1gnj9NFNV48Sn48iCKSsHcQlGDs3YpZghYFQYPMal2AqV6s7vKecGZQMKIuKqrI18MiBLWu8roK5jZpAzT8TcY7EUGkNrua1l8aTFzlVVYG3XNnepNdpsXd0yKuvvVozYbxHSEEQhhRFgZASHUVIUXudKq3rdpJxCKUwxhMGIVKcvTM5OBqipMAJzY0TSTMJ+dim4krH8dbBgK/vTFnpdNje2iTAMikK1OmUVAZgNSqKaTQWmEk42r3FuD/mYOTYGTr645w8hJORZYGz1yGtJJYRKgBTThBCoqXBBAXWj6jGGdYqtAlZXL7EaaHJJxO0rAfapoAiwpU5R8M3yMopQRxzUp6yd3qfK2H7Pc3pvcNAkZTGoiR4V1eu+Brk8RBvJIVDYnloQFO72QusDMisYlI6tFcURpBXBu88U6txeOQ5BaL3nqqqiOKazhkED42Pa0je6nqb2UDiKocSgjKrELpgmvURytVSWgVWCaIoJEoCwlChY0dlchIV1i5F760Qf3+T+D/9mV+k0ai3Ko8wsPNRe4Gu7775N99Jxg4/56w8pJPVpJI6mVhr+cqXvoQpKz7xye+qt83nrOPlDzx0VKmr/W/uowsh6u25d7VX3/x5bvwjD6Jv4jD6udT9HfNngZgbBPhveij94dHt9MiyKXlen1AHQUhRlDg/d8W2tRtJM41ZXerRH8Jit02SNNjc3MLa+gIKo4BWL6UsS4pyTBREBN02eZbT7SwwGo3OXMeVratMmw2i5hqrK0topQmjDRa7HX79F34WMT0hXVpERU3iOKl9NPMcrSSRkix1O6xfukr05HNY4/FCogONc448z+vq2dpzD66KosAYg1QSZzz+4dCN9pROMi1yQhmAFqhQ4pxFNRSNzdrVZ5aDl3XryBUKWzmh/sroAAAgAElEQVSUd7XMUQJRvYELzmF0OFOfIzgV1UQ5agsCaz1pkrK/f8Cv/fpnkSogbUS1qYYM8VLRbDbRSj3iXwihkDoCHc0BZYK8KLHOYc7ZmaxtthmNKmwl+LHvfYZ8XLL7YMLGxQ7r25Af77Dbn3D/xiFppFlY7tAUFYHPUMWE2zf7PLWZcWGlwa1wmdUr6+wdHHJhRXBz94SXLqT84NOa9YWzOe9J1KATr+JCixYlVeXQ2pEqcPaU5eV1tIjJFi4QeEs3gF5WIaRCzltHnpLKZpzYr7G1leKqexS24vL6OkGZgq/fm7Oiqh46zGu80HhRD5HVhZahPjevkcrOPyyh/JxuqjBA5Wu6kjUxCou0Bm00WMdp1WDsAorzGD/eoxRYmwEarUKk9kSxpKo8cRSStRVJE/bvnTK6f8jaUw2iKH7UClRRgI8EC0sJYSRJGyFBrNBh3TEOtEAHZ5/dPIz3NYk3Gks4X1HXR996IlkbM/zBm/xbv/ZOCvVzY4DpdMrf+Vt/m2eeeppPfPK73lNfbd6XeeTKAv7RQIyft09AzjXX9Y8/dI3/Zqlc7Z7zjpmCPAez+/tjMp6gtSBNYk76A4q8AqGQKqAyljSK5lWFxGKxxrG9vc10OqWqcqIoot1uo4OaiRyFGsJafTEZT3GmdlcPw7MlhmsXL+IWuyTNDs7W7t+T8QgVxqysb3PtrTe5u3fE9jMvsr6+iUcQBgHtdpNLG2ukaYzWITqMCeJaiqbkQ6SmZDYdPXIPOiscJVILlPIgQcxNoI0xWG9pLTRwOIQUSKUIhK5hWWF9XVhrsXOwmdZRffhtSrw3CCWQAQgD0TmXvakMVVnNkbQFwvn5QbrkzTevc9Lv17sdqeZnM4JAB1jnscZijUWHQU1kVApjHVVRoZR7xH9XvnYnOit66SqdxDCYTrh++4huFPDUWhOUYnVScvNY8OylkODpJlMCekstTivF8OSY8ahgkAsiXxAVlv79ESMt2DnNqWzAejPko5s9uqFA6bMr8W66TWivsnf3ECozh0mJ+cFsxUy1QChy5xCy9so0lUZp/ehzdy7BmCZSSkayViB5H+B9QqAkIW2i8OyHiXUOOU/Q1hgq4bHS1sWWqHXmf1ih8I7ZicA5KLxHCEfhPVJqpLd4K+sE79z8wfDuoZQiCCVKS4qiBFWfspRlgXMSJSVBJOktpjTTlK2tJXKXE4TBo2E2i8NHgrRZX79hJOviSUaoSuGdm1NTz48/Aoqh4g/Ar/j90t2znsjiW/6+v7/PNMv4j/7sn50foJw/q/rQG1KIb4ZlPdyq1YM93rtHIvxvti1Ttbj9m9btvymJ/8GvnRWmMoRhTBhp8iwDFI1mhyCMqCqDVJqqKBmPJ5RlidaadN4aCYKAKIqI4gjnagMCpQTF3EAYIZBKMhgMz11HZQ1BnDArc8r9/Xn1ZNE6YPHCBjeP9nCzKePhKbNelyBs1GYMEhqJYml5iTRJEUrPNcX1e6a1Jo5jrMmoKok756KM4xjvfD2AoWRNofMOZH2wnLsK72qfzID6YVX5ivohXD/wna8rekxOFAToORtaa1Eb7SIQZ5/zzlU0tflxZX19sG1qxGx9dqbQQYC1Zu4wJOGbrqdAa0pjkLpuuTlnEUiqskbYRlFEWZbvQa3jGeaG4+GU/vEJb0wll5ZTAunpJhG5s8jA0us0WEx7EEaIbEKjF/D62IGGybTktQcTdgYVgXB0ooR2q8HzV9o0WhqNY5af/bmsLTzNM+uG0Oziineu/ZofYhA+xCNwcv4wmO9gwX8T31t+S23zsBwTQmBlwVJnhQsXzh6O886BqhOcsbVqSM5NqcX8/v39MtZvdayan9MA3pvaHk9AIDxYi0XPD2LP7kUbUx/Waudrj1VdP9A1EZXzZGWBUoqw4YkiSbubEsRLBEpivSUzBZUzaC1x0lDZon4wqtqvIIw1wvtHTkbnhfgPcWt/HI/jcTyOx/FHG+8RsfI4HsfjeByP449jPE7ij+NxPI7H8Sc4Hifxx/E4Hsfj+BMcj5P443gcj+Nx/AmO91Wd0v9LWz6qTpFRQJDE+AAIBQQSQoWMagkO1tSn+qLmxAtrwdYMcG9qHofMTQ1nt2Aqhykts3FJbiVlaXnhZ4qzJC7v52nuu67jMz/4A/6ZZ57lja+/wu7uXfIsw1hLI23QW7zAyuUXabfaKF/y9a99mdP+Htl4iPMWiSKMm0glHtHllA6IGh2UjhBC4lxFNh6TZ0Pu3tk/8/1wtuR0/y77t9+m0WwRxwl5XhHoEB9qqrJ6SA9GSEEsQ3QQkpUPzTFqPXUSJiilKa2hmisw2p0ujfZCPUAk1buu4/P/79/xeZYjBDSaTRwwmc3IshwpJFmWE8Vhre13tUogTgK8sZjK0uoucjqacnB0xNWtLdAB1vpHw1R4RxKAVoLP/Ln/7l3X8YN/5id9EIYopQnm+m45l5g+/AM8+poXAss7TuwPf6aW1tW0uyTUdNtNlnodtjfWCLXg6GCfP//f/jfvuo7v/uj3+aef3GBtuUs1V6ncuHdAq3uBxU6LBqegI37ni19iaXUdLxIGU4GzZf3aZY5QDtfocSGNmOUVWX7C9mrK9uUn+MgHr5DEgslwyH/+l//au67jL/zUj3tjakRFo9lC6wiHoKoKBNDpLWFMgQ4CytxS5DlSBHjnsDanKGaUVYnD0Gx0MKWn3WqDt+TZlMVuj7TRxDjB//A3/ua7ruPP/fn/2U+8oqBgfalFur9PsLnKKOmhWitcWmnTSCIyC0YEKKVoRhVIifUaL0MQtewxkq4eKRSSAIumQhJhpUfh+KnveO5d1/F3fv6eT5KIIFA1OmE+walCjVSCxCuqqgDl0VGERyFsAG4+++BrL4VCVVTW1ogK63HOYGzFeJpRVgbrBX/1x999HQ/jfU3iibeEvkJ6Necx1JJA4SU1Baq+UJzyyEChwqDWb5v6+7ay+OLhSJZBeEPoBZEUWAFSB2gBhTp/g/FQs+weyY8efuehSLGWq9VfmcMXvzkfz/99lrrnPI+8/fu3mQyPybIpRe5opG02tzZ4+9rb3N+5w2iWE0YpgYKymKGlZHl5jdbCci3nE5LZ+JTJaICpqloyhUcEGikUJqsoivG5zBKAk4Ndvvybv8LBtVdJm23CoDZ+lVLR6jRQSpHl+VwVDZEMQGrGWYZqtVhY36DMc7C156hSmpXVVXZ2dgijmKvPfpBLTzyLjN6dELewsDrXls9H3CcjmmmDJKqnIlvNhDiM8da/Q5WsDEVRMnIZZWUJ45ROdxEVhIRBbTAdSIjCiKoqaaRxPWF3RoRRPB/bFwTSIDG1P6hQIPRcFyyRyLlOzqPVNyV5Hs4xCJAK5wTGOWZZzlBrptMpLlS1e/sZ8czVLbY3lhC2II492cyzvrzIjZ0D0jglTdocnYy59ORHWLt4kQfHx/RH+9g5K2Rhscfy5jq6s8RmUDEYzZgWC4we7HJz54BsMmb70grrqwtnrkOpemjJC0GWV4RRiFL1vRmEMVHcJvRlbccWlrQ6hihQeGdwGPIioyjrQa6dewPy3LOwsoX0BTrULKytEEXxuY5L7V6Mspapk6y0GgSDiGihTSPtkiO42IClXsQorxgXJUpJLi93ETrgaDBjXFQYLfFCEMp6GMjjCbFEwuK8wwiHOmfSeqnXQgcKpcR81EjgpK8NsiXsfO0N9g92iTsNnnjmaZrNLla7R9eGs/WDPcRjRZ3ABR45zylaCaQM3tv4Oe9zEg8utmA2piKqBy9CjQglhLKG9wcKLyVCaVz9G9Wabq0QUYh0Hm8cGAuzAG08vnT4zCClQY0rVJYjzqGywbw8FgJbVVDlCGfwVYW3FuPrwX8fhUjqKixKU/y86vJAME/17+Ym9F6km09cuUq70+F0aNDhN0jDFpvr2/z4f/KfsbS0wF/6K3+R1UtNkrTBfllC3KayY6b9fVa2Pkphp+iWptdZpZiMseUEHcT0FrbwMqbynsWLz3B6eOvctbjplNHd25j9O4yVIgxCQFBVlizSKKVrNxepkFqReUHUaJA0G1y7dpdXXnuL5eVFLq4uEmrF9uUtEmWZnOwRJymT/grFdAN9RhL3IkFq5jAwuLNzwuHBES+//BGiUHH6YI80TWm2O2RZTp4XVN6gMXSCJnEcoZRifXmJTrfLYDQiUIpcKe7f36W11KE/6OPP4bxHeMrRPsdHtyjzQ5TwaO/BV6RpwtbGcywvb9JaWKW3sMRKu8WCgul4iCtzIgWNOCCrDDeP+pzOCkYiRCaKZkORpi0SCVN19nDL0kKLNFb02ksoHK10grl+QOxKhoNTWp1tXFhXgpOsZGXtIs55Dk8f8IGXX0aFIYPJlM0LK0Rf+Vf43JIGIU9e7LE7MJyUKfuv7tKJ9/lP/+K7r8PIbq1rDmOavQVavUWSRodGKyUKBKGfIqVFCeaJyBIGNcIZJCcPxrz26tu8/tpbfO2VV5HCUw4HfNuHnqa53OOJF59AKck5s0+Eiw2ioqBHxMnNt0jGBeTraFHQaQqq2Yjj2SkiCNAqQqMZnxhKY/FSsJAklAiQIZF0REoQYkjTBCVT9gcTDA7hzh4kaKcaIee/qwTweCWQ3jE+OOZX/8HfZzQbEPeavN7p0AybrCx2CeMI6z1hFJK227S3tll94TmMsUgL1oKxEi9jnPOcQ25+FO9rEheBRMYBQodI4fBa1C0VbfFBLdwXYo6EsxK8wrsCi6sBRA5UYRGlxclaHC+DEOVDpCpozgSRgrh6b90SD+z+6mcZPjikmE4wRYHzHq8VQRTS7jSJGy2CRpvmU1dQVhC2egRLvXcqrf+AmE6n9BYWsLaPFCnWOW7fvkUYhiwvL/Hyhz/Ca6+/ShilNLsrCCEY9qdMcsPzFy/x5mufI1ApTge0mh3KmWd0esg0CIk7q1RGU5TT9zS+a51D4LHMpy3neAMUoAKEqgeftI7wUhKGAUGSYNCUBox1GGPpdrssLnRYWl6iNI7RLCNptNBhiNTnVcD1tJ9U9TTt9tYWe7sHXLt5lwtrFyizgoXFHs5ZpBR1Qm8E9ZStkARaP2K/oyRRo8nJ8QMODo/ZWN/EK7Cxr6FJZ8Rk3Od4/wbH+zdotxRR3ISgCa7k8PQUL2+yvdzlpZVtQmXIxwe83d9jMhqy0Ei5sr5OmEpOHvTZPbxOfzzDq4ik2SP2M6pug6yYcrB778x15FmO9ymLiwsMT/oIIYjThFarweJCk42LC0QxPOj3WVjskEYNxskRVxeu0llcQkQhutGkoR3aTFno9ehPM0Z7d5E0cc2LoJrc271z5joWVxZBeIIkZHm1Q9pIgBIpMiSWQGmE0EjhUdJjjWc4mDGeFuS55a03r/N7X/wK9+/vkU1HRGFIp7fEpSef4/6DY7wIEPIhePzdozSOSEAxnpBPC7zzlJMcXWl0oDgaO5QEFQhkoABDFlqsMwjhaHoIIwiURHqPMw6flQyKWZ1zQo0WINQ5bB0JUOMc6t2WxDrD/r0dXvv878B0UncdqhI5mTEc9pneP6S7sIDxFmMNupHS7A/Y/ODz2MqCdWghajtBZzDGYf44Tmy6cYWwHhc7iHU9chUICAQy8AgZ4q2bT3k58AXMQKHqaTjrcS7CeIkpHyBdSVSFiDKAyuKVRKYhiTu/nVIhYDrjn/38v+Egz8nzrH5tK1AKWu0mnRCez0vW4wb5D/0IrShFNY648PQ2F1dWcd6fa7hwVly/eZvJdIZzjmazVyfRKmc0POXk5Igf+ZEf5fDokDff+AZ+bwfvHEkckiy/QCgNx/dv4DDYsiIvCkIdoOKEaVERnhyhowTnJe/ehX4ndBgShJJKBhgvMK6+WIUELR3WlUCAswoh6r50WZaMy5LTwYR+VtFoRDSbCZ1uE6EEcdyk9BKlQ3SUIM5J4nme1Vt3I7DGIqRg++oWX/zqq7z+9pv86Pd8lKrM8bLuNwdBQBTG4OeTmNRTfWI+Eq+ThP7pDRYWl7i8faUedf5DAGq/P5J2h2axwmQ2ot1JWFzeIEy6HB8dMJxWXFm6xMeff5FAwXg2YJqVHJ30SZSkkTTRQcS9wz57D/rsHvbJq4qVpYCQjNP9m7z5yhc5HR5x3O/z1/73v/2u6zClocgMpioZDsb0T09xoSRshDR1wf3Xv4zRIYHS5NMJ4wd9stEJnWaDqigRMiCNExIzAKXpNlIWWg1u3LtPNh2TT6e0Wou46cmZ78elrWWk9uhQEYWSKNSEUQM8OGspsgrrHG4OXguDiP3hEW+9dYP794+49tbbHB0dMh6PwVe0mi0W1zZYWr9CpmKMUUTRfFd8RvQakp4MGQ5mHKmQ3nIXubyIRZFGCiFrPK2an6NJKbFeEWhFqAWJTogDSZgIfGXxWiFcBNrhqEgjVf8+51AdpajbIdI5tFJUpuD2N97gS7/xW+y/dYOkMnhREhDWO5JWwsKLz9Bpt/GiJGzEqDChsoZiNmE4GJKNp1xYu4iSAlNVjGcFWX5+GxTe5yRejmZIM8WXjsAlUAmEkZBEoOI5LL/C6QwhBd6rOml4gfIG4QqsHGKkoKmbUDUoigqbjZEzxyyH3FQ4K7lwzlq8dwxfvc6dxhJjmTPVGbYqsUWFt4amiBFOsOc8L94/whwPWFkM6CaOwc07mNKweXG9rlQf0rz+PQvzZz74cRppwmg4xFlLa2GN+zdfxVhDFCUoHfKX/8p/z9/9v/5PdvZ2CNAYoLl6hfHpDs++9Cl01MA7w+mDnRqeZQxlXlAVM4aDY6qqOHe8G+B0cMpoNKxH4+1Ddsk8kQuB1gFFYchmE7xUJK0mQRozM44HgwnDosLevUcUfpK93R1aC4tceuJZXvzABxkeHwE17+SsqA1oazZFzY02LK/2uHr1Mjfv7FJaCVVFohXWegIdYU29IxK67tbXbWiJEJ4HB4fYIufK9hbOlngdAQ5nz745knSZratLbF55kSiKUFJTzCbMRiOKKOT7P/0xLq8vs/fgCGMNo+mUST7l4toFmknAyfCE67v32Ts6ZjgdYJ3j1u6I0TTnwWBGPp3SjMGeU3kOxxOazYjd+/vkpqI/HaPChMhN6QYRO3u7DKsK3Wiwd+02UdrgwlLKYO8uAIsXtyhshWFCGATMipxnn3qCrCgodg+4OTiBzhJpb+nMdfSaEVpBHIWooEGru0qcdurdV1lyuL/HyUmf4SQnihv0ejEi6BGnSyg5IM8KppMpzla02w2WVxYIQs20qtjY2saYKWVmKLLizHWkVUZ4cIJ7+w57/VMWeh9gfTFlWlnCwBOGilbaRDmFUBKnJFqHBKGYG7poLBXWVkQmQ0UJ04bEF54CRVTWucblZ98vSjrwFoWnnE147atf5fd+/bfo39whNDVvKSejrBS9xWWWL15m3AzwjSaFGXB/dEwQt4ik4PXXXyXPM5zzBHFEEAQUecFsVpK/x47C+5rEfWERlUVKhwgsODEnFwqwDm9LhBJoIfBWg02wQYXVDkuEiloExRSfw+/d0dzdrTg9NHzsySZPr3p4MEN4+57AMcVoyN5wwEuf+F6O9u+QTSaMTk8ZDfvksxwdxZS24KDVpRSwdf8OTod1nzzq8frbt/AOLm1t1Fsqcf528PfH6dFdwrUt0vYypV6EQNFsr1GWJUrHjCdjnPd813f/GJ//+i20lJzcus33ffsLHB3eZf/WfSim2KrAWkGzs4ZWEUES1K5DVW2QcN66PNButel2u+T9PkqIGkoUSLRSqCSk1WkwGk8ZDmZ4FeDCBi5qc3JyzMx4pA4ZDMe0Oh1u37rBrDBcfuJ5Nja2OD04xFiDMYYwevd1zLLs0VqTJKEopjiRsbq0iCkdJ6dDep0YcFgLRV4RhrXzerOZ1gRJ58iymtN989YNVlcWsS7ndDgmanRRSmHPMckAkFKjdf0+SqEIlCbY2kSaY66ub2AKQ5aX5GXOLJ8glQAlmZQlJ+Mh948OGU2mSCFqeqGEIjJoBVGsELGiLM5mYwwmExbLNtZJvKg51no8YE1XrCWaPZOx1kxY21rm1TfvMB4O8XEPSsv0vmUhjWvWuTKEScrhaZ/l8ZRLl7cZj4aIO0ccHyUsLK6cuY5uMyXQslZ7LGzTXrjwCEJljGE8mVLs9Xnr+j6ViEmbE4Tz5JlCNVdZvHiVwXSKnA34tm//MFtbWyBKrl17kw9/+MMEQjIZzFDn7NSqrORoZ5+JUKQLC9gwYjQeU0lwrm4ZZrrAGVFzd7QgliV+anBS4FWCwBEowXJgaJiS4xv3iHpL6MUlTrMCKQTFOWcmUtRc+3w24/Of/Sy/+5u/TXU6JrUC7WHmKu6PjpGq4smLS7DU4u6t6zx15UlEoDkanFBWJ8RKcu/+Xay1BEHIzt4egdJ8/GPfThq+9x3++5rElQpRpYZK1CoTV0OAReWQ2lDNGhjvKZ2lsJLjwZh/9/qEKRF7w5yqgCAULLYU3/NEzCeerFh43hJbh8ipqXbS4oOzS+Lh7l0e/KOfJrmxx0vmlFkUoLY2cM+tIze+k9OkRRi1GQ5H3HlwSHHrBptf/AL3dvc5fPo5VnsdNq8+wVsnI1qNPgvLSwjqvjLWocLgPSX03tVP1hWkLcmOrzHO+8RaMegXpGmbO7d3cN6iFVxKJ/zbz/0OLz+3zeW1gN/4xd/g8OiQLC/RgSaKG4TxIWGYoMMYIRXjkwOMyXGmBP7eu65DAGEUIaRkNB7jPYSBJtQK7yyNVoPBaEjlAR2xP5hyb2fMwdGrpHFII47IsxlLSws0lzf4of/4aXZu3eDw4Ig79+6jVICzDmvPTlqddptAa8Ioot/vEyWNWklSOVae2uJLr73G3m7Oxz/6EkJYsmyG8yWh0swGUzySe/f3+fIrr/HSR15mdWmR2TTna/s3CYKAyxsWFUQgz04WSte4U6U0Urj6EEsntKJtkjDg9bdv8qWvvcbO8BDrcsCxurhKpBSVtdw/eUBelsRBSEem4D061CRJiJaevf0xw0HJ6GxTdT7wsY9y7/p1VstlEhnhqpSOtaShZG//kExaOk1JqMZcuRBwcDTimbVtyrxg73CXnS/ucDwes9Tr8annr5Ax419+4XW+69Of5GN/+se59vf/AbeOb7AzPbsCHmYTnJQ4J/jqL/xzNrY2+O7v/RStdpsKyf29Prdu73F/Z4eKkGTBMJxkmGJGEoYsX32B3sUtOuGUn/ix70LKEIRgNp1x49rXKGZjLqxdOJe2uTc4oHu5Syaa+KnlQGhmt3eJugHtZko1FZwe5CzEIe1mk2H/lI2XPkbablAUQ4ajfRAxi6ubRM7QU+BHY7JGgJsJsiQB/5CK+e7RMRWD4yP+xT/8h7z55Vdo6iYdFSBMnYxnVcZoMmJxcw3VShnmI27fus6l7as0k5R2p0UYhuAqxpMxwsFHXn6RZqvNg4Njhgf32FjfYMjZn8vDeF+TuPUg/ZzprBQiifFKknvJuAy4ddexd1Jxt19x3J8ymVpcolheczz37AIvbC+zuVjQDkvCUY6wlnLiMC5EaFNrped+EmfFnRu3ufkLP89ar01MjLQDohtvUhWOMgjoffDDqKcukyx1WbmwSXnS5GY+YeFexr1mws7pInZWcfXKBr/0i/+Kb/vQ8yw/9TSNi5ehGSOtfU/yIHf8dSo8VTFhNBiwtfkMM1PS7G5RlBmj0ZA4SVhYukhr4SKXjwQXVir6eUB786M01jLy6ZBidsrk9AGuKpgWs3mydAglETJEnlX+zmM0HDIajVBBSFlV5GWJ0glyTo0L4oC8KJlOpuzs9zkVLUbTjHYzoRGHBMKytrJMaTwiiMnyglanSVVZnLVznvg5H4yvwBmEg1/55X/LdDZjcWmBXm+BpcUlFro9Xnv9G3zltWs899QVYh0+ck+SSnN09IBbN++wdmGde7f3+J0797h/eISKIj7xiY/zgeeexNqKyp5diUtZGyvXksEAKQQCiUPS6G3zr7/wS3zp9W9wcX2RdqKRVIwmU2x1RFl5BtOM0hu6bc1CqpHeoVWAsxWBsyRRQGU97pwL9UMvv8ydazcZTzKGNue08jSUpAL2C0Mz7RIpDXlJM9Ssry5S5TPSMKCTBBhf4toJWT7h8EEfZMTUW37v7Xt0L17gyqULDG/3OZ4MzlyH0gFSasaDGa+98hXanYQoCojiGErPzu4++wcPaqqjVEhn0FIghEP6gkDFJM0u66tLBGE4V1140kaMVJI81QhpGY37Z6/DjcnHOYvNiFM/JR/3yY8PuPTERXqtdcbTKWI2YUt1uegFdw5u85H176e1ucz9nbeYhQopNV6MSeIIHQVc+p6PMJhMuXb3DoPbM2ZZXsuPP/3pd11HVUlmo4zsdERoDUJUaClr+qEpEabCVRXSSyKhcEVBlWcwR+kqPBpfCzOwBIFieWkBGSryWYw9mtCOBLE+W730MN7XJG6sIZgbKQwn8OUblnvDksNRwYOhI7MGoQOaacT2hYhnFwMKofj4dzZpNgt0OQIzwglFPy9pCEkk23hfYo1D+Vpnqcqzb47T0xOOhvukwyOSi5sULideXKOjY4r+KeVrX6H4/34bHQcU3S7TquDWoM9TcZePffH3uBkHrLz0QVbJkIcHnP7sW4yabTovfZjWC8+x/LFvf0/t8WjpKXTcoJxmRPo+/RLiMOX45Iint1f5yMsf5fqNm8yyjMFwgLQZ3/uDf4Z//DO/grElQnjCtIHDw+kxxhqsdQRSgQwoqxJJ/o7ZxruENSVSOMoKlActFIUpmUymBIHGmxJlJZWM2D0eMcwNPiiQdn6hxmk9uDEdMz45JJ+cYoDu8irN/ROGsz5ZNqMszzmoqaYoFTKbFLz+1k32944xlQGlWFhc4Ls/8RKN9iKvfv0mu7sHbG+ukTQSqspy8uCEfn9As9Xh9s1d/t3vvoKUMWmjQa8XkRVlLZFUj3Rh7xoPE/hDFPFDFVJtKlPbB2ocyjoilRnTW+wAACAASURBVKBUgDGwNx1xdJpxOJjgcSx3Ur7zxUukc79PFwqaqaaoFK1UUlRnr6PR6GJ8wN6DKVNriZsLxM0eQRZwePtrvPTMRwjLEdP+XbY2VpFJi9ff3iUKIipf6/2c80ghuL27Q2dxHR3ETCrJ1+8Pef7ZD3Bj+jrV9YMz16GCBCEkphqRJg263QWa7R5R3MLYnOPTMbPC4DxIHJgC5WrdQqhkLdFUNb63subRrqyeo3B0uh2KMjsXvXpw84hQa5rNPYSxFNMBm6tdtroJLTcjKMe42OEPd1huR1gzZGfnDm8eHHJtZ4fNC2tcUI44P6AcjzBaEMQdmoFisn+bwc4hx0VO0j7boHhnmDEYTJmVFc5WOFlhjEBUBhUEVK7CSUBJiqKgf3SEybJ6YNFY8smUoOFAObytcN5iyxwrJFJ7ppMRVBnRezxje38lho8Q+p5+GfONeyMyHdFrNVhvwjPPBnRbil4KrdThCsX/8i+OeOkDCVJO+P/Ze/MY3dL8vuvzPM9Z37325Vbdre+9vfd0z3T3eOyZ8czYY2xiCzvYFigCAwpCkRASiRxBIAJEACVESRBWEAI5AZEAQmBbeMksxB7P1rP19PR+91v31vbW8u5nfxb+OHV7JkBXNVHUsqX7lUq37q26bz113nN+5zm/33fJUvBxqDBCWjhOFfcfaOKi5MlFD1EUGOOgcKfmmc9XOU1dUZiK5sYy+be+QX97DxXEdT7ixXWKXsS81yI6GjKXVXxOK9rDMXN5yuOtFu2b9whLQ/zs89CJKKcTju/cYHb7BvHaOo31jTPFPsPtHxDGLYKox8L5p/BUg7mGIJmM6Mw57t5/wHgyQSa1yIZ8yDQ3VMJnbf0KFtDGUBYz0sEeodM4B73Vq8TdRZzRJJMRw4MHp65DeQGNVpPcWqxwqEASe1FNK/M9/CjCizwcAYkeIfyIKPDwZB0OETfb9S7T88iSCXFjhc3LV+gtrhA1t5l6Htpo7BkX6fD4iFa7QWklCyuLDGcls6QAIWnOLfHt197i0uULjCcTbt+9y6tvXmdlZRVdafb2DvB8n0azRaUt0fwy2kqavTbz8w3WVhbQZYl56CN9Ch76XMM/GSgAdcvs5ReeZb0tmO+2KLRmOJ2wd7jPeDxhMEpIk5LI9/BcjIdEAZ4f1Jmkts4kDfyYojy9rZNmmjDucTg8Jl5cIwx9+oM+z128wI9/VNAMY2KrGNkBS8uLJLkmiBooL8TqCqkdUShRUlAmI2ZFhi0VotLs7A5prTbZGmW1Z/sp8PzoPTGK8nycUPh+jJA+lqL29g5CVC5xVoOpoDIIW4djOGOQgcML1Un8nsTz5EmQdUWlJVIGeN77awgAOspjOJowOu6zvNAkCgIO+wMGu/tEseUJv8Pe9JCDZkj3/Dr7OqP41iu8OdWMtCJbmuDOr/Lzn/tJKFK8QOLLgIP9+3zvlTF3jiakQYganz7YvNcfMjsekZYlxhpsVaGNxZW65sBToaXFSEdWVcxKzeLiEmHoIZzFVAXOKIzVeAImoxF7D7ZR7Zg8K8jGI7IkIThDlPbe+/OBvuufEZy1OGsQOFZaFb/64x2Cpk/c0CSJZj6q0HmGSwu8yjFIu3zlTs5nXhvy2Sc0ohlSEDDoe/wfXxuRTA1e6Xhh2aeKQU81FQ6/PP0Wlu/tkrsK7Ydcf+MGF5yPMRaXpygsw+sJXjPGqBClJEG7zUblUZQ5utfCH4wpb9+l6h9SPjhALfaIl5dYXVvHBZLDr/wRC5/7PHMbG6euIx0ecbh7H4RB519E+QHXXvwFjPE5KKecv3iJ4WjEwuIy5zbWuHfzOl/83f+d737h93BCoDyF74V0e3PUmaQCZx2DnXexDyyFUTRaSwh1Nk9c+B5z62ukUtctCuFQSp4M7AL8wGd2NCUtDU7Ur+f7PkmSIIRgeWWVyBdcvHiJhfUNCBoIFZMXFWGrR7vTJY5Ov0itrBONPF/yYy89Q2kFdx4c0mq3mVtZIp9YFhZ6RL4kL3JKq8hSja4Mvtek0pY007Q6bc51uhR5xsbKHJ944SmeuLxBkhXk+QxzhphD/H9I7B/CWMv5c4u8sPYSEsfBeMbd/QP6/W2aPlxYahFuNui1msx323RbTbwTfr21Fil9huNRPYA/Yx17e3vMLS4yyzOm4wFShzxx6QKZ57jw+HP4kwccHY4h7DLMAypjWTu3xvDoEGUtTgqUkhgBWgm0KRDWY7EV4UvD23ePcd484Rmp6lL4VGVCPp0xTmtbgyzNaAchSgmWFufxlOJ+MWU6m2F0QWDr308XGiiJOyHNhiLw/PfOL2MMeZ5TliWtVgfXOP08nY2PmI4mOKkpGjm5jhmMDUUl8QKfQTBlZGDN79GaxHxzS9Ppv0kUNjDG505/B7+8hvzcTxO05vCEpen7jMOI5nyPG5M9Ss9i9enDitu37zLaukOe1vRgTIlRCpyhLDUVBoetU6GCiIvPfJTO4i7NdgwixQ8U0q/bds74zKYpt67fpgojsrRgXXpM0+JMquNDfLiKTdlEuBRXeoRyyrxSqCpEFB7W1qGjwgsYFm3u9Q3fuD7jrczxX/7BMb/z1RAnKxZajsc3Pf61a026fg56RjFLqFJHNbEk2kAhaJ2yjslkwExkfMUGeE88wxtvf4cfa2VEXoBnBBQlYZlR2gyMxRz3kRZkECBW1vljZeiPB3y8GPPJf/6nuGcF+5MpjaOIrtEc3f8aF95+lU/9O3+J1vrF911Hc26OzWvPMZ6UWN/Di1dYWTnH9v23mflNXnv9bTbWV/nMpz9JFHpsb+/zM5/5DOcuXuPLX/o9pCup8gxtS4LGHNpYkmJaC5fyhND3mJYpce909gHA4XDErQe7bF2/jRUKIaEscrSp8E1GGLbItGBSGJwwhC6g2WySpilvv/0OL3/8JeaXenztG6+wevExnvnYx0FP+OM/+sdcvXaRjSsNPO/0022UWNqdRXrtJj/58Yu8/JGnKYqCNC8oyorBeMr3Xv0uAsHzz1zh4GjM7Tt9yrLC2IorVzb52Z/5SdaWF7ClIQwDfM9DKkVRFDg8VNQlnU5PXYeuqvdaKQ/bKe89VQkoPB+/0eL+7h53tg/Y6W/TaDXp9LoEfkAjimhFMXEUneSVRlTGoE2FRwGcJAKd0da5e/c+Igx56ZOfIo4CDo926S50UTLkK99/h6X2MnErYrUn8Jwhzyc8+dR5bLGGS1KGx2OOJimjWUrVCSm9mGyQkeQ5Ji2ZxC12Bwd0ms3Tj4cuKUq4c/+Yrfv7DEZfYvP8Jr/8K79CI1ScXwro39shHW6TpQN0EtDrLKEUjMYJiytrPH25ycXzS0D9dJNnJUVeEvgtcjciiqB5xjpuTQVOzaEpGRzGGKUxvsD5mid6Eb/45GOYwnL1xY+g/BCzv0HZvUCFoLCGN27cxezv8ff/81/n3/z1X+f3v/SP+Tv/1X/Pp3/6k/z0n/lJml+7RXI4wj9DSTs6GmLMlLIa4cImY50hXIWQdTByoUsaoYctCwKlWFtdIfAkRaUp8pJ7d4/RtmJ1c4OjwzEXNp7h9tZ9KjkmyzL6RcU3v/19yjzhL/z5Xz51LfAhF/HZMCfIMoQf0FCCYerTP5JMcYzSnONEkZaWrfExN48KkkLzay8usEDBSq/NCxeaXGnOaPklYjrBjEtMWWErUEYiPEXhbG3IdApEu8NIa8r5ORa7PQ4bLYbjY5b9ACt9TE2gwUcghY8yPrrSSKP54+s3+E63Q+/KM/zW/ds8fesdgqVVoqyERgPXbTGZHrH/9ojp7XunFvHeuRcxZkZrLiSfzBB6yv3tO/hRk6oqcE6wt7/HW2+/w7UrV9hY3eT1179Nb+kKzYs/j0jfxQlHmQyZHh+xeO5J1jpLeI0OuIqDu2+RTYeoDzAgCf0AhWQuDPCDgKoqMNLHWglaEDe79CcpnnLoE88YXVU04piw0eDe1haSehfc6XYJAp+7d65z+/pbZNmYq48/cWbG5h9/+3U+/RMfZ3FtgwYFwnhgqbngKDoNy9raBls7ewROkWvAC5FOEQcRV69eo9FsMUtSlBO1F8ZJP1prXRsfRRGdM3qeylP/RC/8R4u5kg6Pkq2dHXYHI/aGhyR5QjOo6XyNuEGr2aLdaOH7PuPxmLKsKKqcJJsxmQ5RvkRJH/Tp52maZqycv8Qsz7l18xatdsDC8gJZrunMr+DCiKN+xcqij29nTHaGBFcuEbTaaGEJQh/jDdHWcnycMEkM2wdj1rsXWNq4QDo+YjKd0G6cPviuTMl4kjAYzbBGMxkPeP317/PJT32SC5sbjEZ9RoMdpsP7OJMioja2iihyQ5aDH8R0Ox08KRmPxgSBT7PRIgobWAee30NID98//TyVUmKFRAoFRuCJAN8KrMu4OjfPpy5exs4qzHiALTU/1W2SP32Bo0gyzKcoN6HYuoW/McfNrbv0h0Oe+MhzPHbtcdI0oxM1ODZjxBkPrqXRzNIRuclpN+fRRd1hwNV5n7gKKRRloRkNRyzmGV4QYHVJnmviuIdQgvmFcywubHLx/HkWFtcYTodMJhOaxtHfvkeenX5Te4gPtYink5IyM4igIgs7vHpHc2tQoSX18C328APJtRZ8ZHUJI0I+sTFjWIaMcsvT82OiYobOPKgsOB9hPYQxWG3BOnypOKt7cO6Flwg3r1LlsLS8xC3nGFtHg6oeTkh5YnZUm/54SuCwzHTFD5yhtXmRj730Ml+ucvqvv4rptvAriR81KAOfZn7AjvHYH41ZO2UdZTZAIvCo0OmI8XBKlhyzfvE5EB5JWtJpKqZpBlKytjpPr3eB3/wH/ytVOkd38QrM7hG05mj31kizgmH/DmU2Jc8ToriJ50uy/PSdJ9QFQ6czPF0R+QJhK6zO8RVErQjhK46dJfJD0spiK3PyGNwkbkZMkhm3797l85//Kc4tLSG15u6N6wz6uxR6xmg0wDtD7FMZw9e+/SrjacKnXrhGlmcU2pDrkkobkrKg3WlzzjmkCBgNJnhCIH2Pubk2STJja2uLTjNkY2W9HqZ5Hp7nYYypOeLW4YenF62HRmfW1gM4B0jnkDgasYfLRjy4f5cHgyGzbIrvSxpRRBRGRFHjJDe1gVIKxIyiKBmMjxmNj8mKBN93Nc/6jCzH/X4f40ccHB3g4zMYHrC+sYqSDVaXl7lz6y633r7PtJvzMx+7zHKvy7vXb7G5vgS24nBwxFs37tJttFHGkac5hVN4vSVM3GVy8yblLCXePJ3alxcpSTJjOBgRByCVI5kec3S4z9raEoPjI3q9iPDaRWxZ0oxb3NreR4iIqNFieblLuxVjTUGkQqq8JNETGu0mQRBgTURZWMbj4anrCE68lQwgfYMUAVKE5Eax+OxLXPiXf6XezDmNLTS6rNie7jHt38M3KRsbbe5OmqReyL2dfawfcOnK4/T7KV/7+qvs9yfES6sEUePUdTR7XUZHkmlZgkyJVZ1Mr2SAwDHTOQ7F6toGUdTCGItUHmVZsLp+jrW1NZRSjJMZURBgy4qluR5hqJhrdzCzjFBafP+DlecPtYhXmUWUCik8xLTgfEux0mnQaCjagaHZBuk5GjZDqJQv36rYZIiyLSbHJaYrakGQMVSlhcriKkNVOMrCYnNBZR3hGbL75Wc/zgv/1r/H4Te/QVWVjEcTQr8NsSSvclpFzVIsKME5tHD4MuDYGZJmi04cUUzHOOM4MjnBNCNzAl0onHYMhMc3pwUfP4OPnLuAIFpgaiLeeffrCFPiRQ3ChQRXKlaWmnS6HSaZYThJuLSxwq3b73Du/OMcv/kqW6++wsrmBkWREYRNgiAibrcwoc9sKjjev0ORTanOEC8A+GGAMBmRglCBk4AnafiCVjumFApBbUuAVUwnE5J8RqvbJslSrDNoaymzHKE12XjE1s3r2CqnKiV3b93ik5/5mVPXcG6xi3OO8fEBr74lsNgTDnyMEIIkzaiKnOW5LsejHK1rZ0OlBK04JE9T2o0N2o2QRhzS6nRqMy1b29E6C07Vr3catNEn5k1gT4RcQoBwhsV2g/E447CQjAuLIyDwFWEY4vshUkU4EVNZSVYVpGVOkk4YjI4xpsD31XtBvGfx5qWSJEnG6twik6TgyauXWVjo8Mor79BqLBBIQexJDvvbTMZN5lsxf/d/+l3WN5ZR0nA0SRBYPv+RJbxxQlYWWD+q3feSActyirfWw2XJqevI8wmH/T7T0Yj5uS7Lqz0uXb6AcxWT8QjrfJ776Mt02xHOGNrNJn/rN/5b8rxiY7PHxQurNOMAXRmc03ieIm7E+MonneVs748ZjqdsPbjPv/TL/8b7rsMhTpwHJU4otFAgBe0woh353N3bZmN9HWNg5jTLm+u4u8eEMqQpBHRiLjwlKJIZv/Vbf8Te3iHpMGeawjjLuPLkJeYvXCQ4Y8Aathv0ltcphcf2cI+W7+N7CiUFSkBeVcRzc3zyMz/N0vmnKKUAp3FpHR4eRh5lnjHfarC7fZ90NiVNZkySKb35ZbbvPaCYHOLM6e/LQ3zIg02J0VBhaLuKc3GEF2mUV+HLAlWWVOWJGZPUzIsGQQXnhGFp2SOc5WhdYUyB1gJXOWxpqUrQFVSFoLQ1Y+M0IfHOziF3UkNSVrjJBOEk/dyw0Owy35yjtdllMhiQpWOUtiijEcaSaE3Um+Ozn/oUZTrjJ15+nv7v3uRi4JEZxaEOmSrLt7aHTKTAD04/GWZpRHo0YDbZRnlzqJbEE5YqScmSB6y0NzkcH3DUjxkcPODK+Q2+8IV/hPNaNLortM4/zcHxLirqUWzfxFMRzc4CQdyku3yZsL3A9PA+5gPI7rPZlHQ8JKxmlEjKyuH5PlZ5aBmgnaTUpjYIc4K8MkxmKYPJlAXlcX59lQubG6RlDkpw79ZNtrdq+ffF8+cZDoeURclph+SJxy6gFPiez84gYWv/kCTN6LQazHfbSFMym4yZhg1ee+s2B0cz4ihieXGe4XCMcxWtVoeV5XkWFhbwAw+tdT0ELSvKQuOFMZ5/xk784VBT1m0jd/JvnvKIm23eup3xzu6YZDpjoRvR63bwwgChPIxUFNbgipyyyimNJiuK2hLX9zCmIklThFTkxemUS+lKVleXaAYRcjTl6tUnOTrcZjoesL52nr3hEctziovnL7Cx1AURszfJ6d+8T5JPEUGDT3/seZpxg2EwIyvHdFTEgp4Q5Skq0PTWFnnj/uGp66gqTTIeo7OUhbkelx57jPmFBabTCbPJmEuXr9Dpxijf4UlVKztbXXb2d7n2eMjKQgtnKgaDCdoWbG5sojyP/YMBb717l+/+4AbHwwnD0el8dUfdT7eC2udIgLOCy1cuo3TKu6+/Tj6ZEDZ7vPHWW1y5dpW8rJhMBDs7E7Z3D3jjtTe5ffsOGxubFBPDzvYEIX0CH4S1SECfYcuweekSgahQcZd8fEiWFyghkAKs0fhC0Ww0+MEbb/Pp9Y/gNWKsccSNBkjHzVvvcuPtt3jm6lX+8MtfwFQFRZFjheSxx59FlyUH/T0mx6ezyh7iwxX75BZSg/UdxjiqKoO0QISgA4OMBJ5UOOkhreCpRQ+bWjyX4FuHtgqjJc4YTOWwFdhSYCqwWlAUjtyBOkNVfW/rPjfefJVvf+cVnnzmGW7s7nCjTFjsH7DcbPD5l1/k3LUnWSwTAgFOG8rb7xDmlqKoeO373+Pzn/kMRjjuZJq12PFOKfgBPmo6pF9OeeHyVZbP6L2G+Vv05jqwOE+xuYkDdh/cZjzcZr4Ts727zerqCoeHuwibcken5FnGwvoyQXuRVrfLfLfDYDjFO/cRktGA3aMxoTdCyB2aDZ+wtYDj9B0fwPLCIkvzcxwlU6yRZE6B9Ukrj1ZZtzq0kMRxjNAgxgrlh0zTnHZTc379HNeuXOXc5ibGGt76wfeZTIYg4fFrT3A4Kuj392n1lt53DUKCMZqiyOn3+xyPM4zwEbnG2jENUfFgt0+zt8xslpMnGbrUOGPI0hlzc5eZJQlF1cFZR56kJEnC8WDIZDYDqVicX8ScIfZxztX9e01dOYzBKolQ8OprP+D3v/hlBsMh3VaElCvM9wJU3EUKMNQfgadQwsfltZWy9Gq5vzbmhFaXkeenvy+zyRFKGCyGvMz43nffZG11ibWleUR1TDV5wHIz49mLSyz1elTtFfxmE4SgLHLmO3OcX9nEsznGEzQUrLRCutM9MGOmpaXyGgynp7MxgqBZK5GVxAsj/DBEKp/+wTHtZoeN9RUKk1HqHITH0dGQ+d4iWm+fGNkZZtOSLCtpdmJKXVJlGW+9e4s//MYPuHdvFye8M9tL1lqckrUnt7OAxOGYpCUHU0lpDVu7t2i0uty6fYff+b2v4HkNptOUBw922dndx4wlpRM889IypT3ED31ip4mrjChQhL5Hmp2+6YmDDiro0lm+QHJ8n0yXGCvwoyZhI6TKJkwGA7755T+g2ejx0md/BmstlQGLx2Knx7AVM+7vcrz7AM8XKE/i/AZOayhKnnjmeXa3T2/rPMSHWsRX/bBWDwYKIR1S1Gk9embJnSRBIIVDuApHhUfGoQVEbXJkjcQaMMZhtEAbi7FgdP21IocUcNrx5Cnr+Nmf/TQ/8eMvkab/Pjdu3ORf/LO/yu7eDrvbO9y9e4//5fvfZPLtH5AlObbKkcLQQeELqDqWP/v0M3z9W98lCn2GFy6jFpZ47o23eWazzcC1GXzyM3zu53+JLDh9MBF6ltnBPn4Q0GrFpLrL/Pwc3vplitkeMh8zHCW88PyLfOLlF1hcWODFF67zlVfexPox6fgIX4+pRvsUpcMh6PRioqhH0Jins7AAIiD8AHxTXYFRLbbKkMbcIgfDGa3GPN1Om8Fsgs5mpFbhioxWs8lzz17lzp17JHnB6soyP/sLP4+SEPmKN77zXR5cfxtPGqJmyNLCMnNLjTMpU4PjPmk6Y25hiUazwXk/IAxDyqJAlwVBEDG3MM/de/coZhOWuy2UEmTphJeevsYv/gv/HAsLMVHgMRsfUVqojKWoKqz0OBiOGI4Tuu02HztlHZPJlCAo8DyPIPRQqnYCS6VkcXmNTzzzFPce3KbZadFqd6i8gOPCnij1CqwuGQaSOAoYjUaMhwOErQBbO0TKACcdUp0+6P32t77J/uGMi49d45Of+zzvvHmP2ze22FgIeHrBcnEtZfWxqwivxT/80tf5rf/rq4Tdc0ySKaX1GE4rtvaP6MwL1juCjSs9ZpViUqUcpxWt+R5ffecBg/L0MpAMj8kGxxTJmEEoODgesn88YHf3mCeuXOLa5Q0WVleJo4jZZIK1Gt/z6XWarCy10drg+zGNLiRZxf6NfW7eesD1O7uMJ5rewipB3EScUcTfS0wSJ0EVygPps9Uf8tpbrzA+sOBypMnx1AxbJRgXYJ1BOPCkpGxbOk6j/vgVLgQ+y7Eg6QrGvS5ly2N7ewulT6cob93dpioNS2uXOb7zfaLQYozFGkfgSXJnkEA7Unz1y/8nB4NDPvrRl7DSR3U7bF1/g3dfe53nP/VTPPepn6M33yWOIzx8ytmMV3/wXS5t/hhzF184dR0P8eG2UyqDTkuEVkSytsmzQiAR+NLWSSk4jJM4IcFVKC3RAkon8CuLdfXnVeHQFipXF3VrDLoUlJxtW+J7km43ptNtMDf3PFlRMEszklnGdJTw1ltvsre/z4Od++w8uEf/sM+De/cY5SnVZMJXv/o1lpeWGI2O+drrb/J9PwBTogd9cgvnxzmTEvb7Az72wvvfTo727uEMRM0mRrfJXcLyuafw4wWC9QX6d75PHEcI4Tg8PKDIC0pjOJpqemrM4tIq0dxHWTCW/f0h+aRPmQyx5Yxx/wbF3oSs1BB0gP/w1GPSPzjmYJxRIYjjkOUgQhuH0wV+q1cbSw2GBFKyuNhjZXGOqsi4eesueZGRFjn7uzt0Ap+d+1s4XYE19QUEdLptur3OqWtoNRs4LF7gU5UzyrKg2YxoRC2MDohDn+nkmJX5DmWqmc4qGq0Grd4cTz5+lW67ga4yjArQWjOZTcgrTZYVWKHwpSJNZ+jqdE8KeWJ1WzNaLEYJlKoZK+PRiM++/Cz98w200xyPZxyN+9w7LJnvdmk3GkjfYzw+5uioIJ+NsLpEYXDUaTJKhfgyeK83/n7IK0c6y1nodtnf22d1bZH+zhaDgxED1WBShtx/p8/Nre/ynVffwLhWHZriDNKCqRzv3t/iyvx50qTCDyNmKA6mCUp49Mcztg4G+J3Lp67DaEezETHXaaKaDRqNiMksYTKZcXg45Nx8i6jRoCpLpLN02jHJbIrEsr66TCNuMBxN2d7fJwib7O0fcfPONoPhFGssC0tzdRGszjYme3hxO1tzzREO4XKi2JA5jTJVnbDjO0pTYZyHBELrCEzJRSoeCz3aix0GvRDbaxGHHkVqSa1GOUsgTi+LxtQU0XObl3idiNn4gEApfE+i0zEIWxuo+T7aOu7fuc7ksM/axnmee+5ZdDrFmYrR8JhnnnkKYwxZmjE9GjDYu8/x0Q7JdMylq4+dfTz4kIv4zlAjxo7AM3ieQYpaGiykQylLqCRSCIywOCwagTWijqRDoGvv9Nr9sKzfSGsd5uTfU+eorKQSZ0di1IIDQRTVg4Zuu4Fdqr3hz29ukKQpo9mM0XDA8HjAa9/7Djs72zzYvs9oMEEJxe1bN8iMIalOBhBCIKRinExIsxx9xuBq4/lfoEonJJMj0uMHBI0Wgao52u1Wi2B9jf2DfUaDAftxyNe//goff/lF5ubmONi7wf6d79BeWMcFHbzGOkEcE7fnkX5IGARkx3cp732fvJiceTxUEJFan+ksIRhPEFGDJNcMJimttmNv94jhwYD19QWWFxa4eukcVZFycHiMUh7j0QSH4uad23ha43kB1jicdujSEsY+cfP0XnQUxSBrFkLsgUThK4fvWXRZsDA3TzZp0wpCGl7MvQeHz3ZxWAAAIABJREFUTHLD6tIiFy6dY2l5gTyraXBRKDjXXEEqhTaWsjKEcYMkSSiK04u4MbWzpnMOIS3S1UVdKUWB43A4pcwnZNmU2SQlneU44ZNlKVpX4AxFnlAVGbrKwVZUujh5PZ+42SNQCudOfzJpxB3Ora0TeB7j0ZDl5Yhep8Fwa5tb1ZTX3r7J1SeeY3nzGp9orfCtb71BaQoQtae67wUcjqbc6A9Rcy0mgxn94ZBOo8WTV87z9774VXLj453BqQuCuLaQ7URkbR8RGWIRgnRMkynTdEJx5BAI5tpdlpeWCH1BtxkzHg55d5YwGk7Y7h8QhC2OBxMmowmBlDx2eZ31tXnG48mZg17nDNLVCtgSD2EBp1FY/EDiB+DZCGtTyA1dKxGuIMDRkYblTsRqtwexT7+3SBF6lKqgyFKM9qikQ6cF7gNURYejN7/ExcefZ/veu+TJBGdLjC5rZbIfnEQ6WnQ2wYUeb//g2xw8uEWZpYwnI/LXvkUxG3B8NODo6Ih8NMUUU0QzZNTfZjJ3Grfth/hQi/gktagcIiXAr3PlwCEVKAWRrH9p8yM9XGfdP5Fq7Jw4scGsU9oqC9rW5lq5EBgr+ADpbEAtr35PxCHrXqjvw3LQxNgGq2aeslylKi1PPH6N6WTK0fERe3t79Pv7PHb5Gu9ef4MsSSmL+iL1o4CPvvBjPPXUs7zwsdMfh+LOEvNrj+OFEenRA/LJHkEQMB5ukx3VplhKSVqrTaRUjIcjllZW6e5m5BOPSkSM9m+RTIcIJ/CDiFZvlbDTIw874Pforj9D/AFynnxPIp3GWcXBoEB7hryyYCUHgz2mwwStJcNJwcFgzGc/9SLKE/SPhty+t8cf/dFXuXLlGnmSs9huIKOISkis9EmrkkpXZ4p9tLGEYUCv2yEZpxQGGp02rUaDopkym0zJs5wwCllfb6BtwY2b26wvX2JpqYsfeOAapLOUKPaRqhb6RAKMrr06Oq0mxRnhA9ZqpDxR1FmHPaEcCqAEDpMSnVWMRjMGk4SkKIkbLbIiI8kSqjKnLKZIDOgciUXYEuV5+L6P74cIYbCcvvP0hCRNEl599btc+8jH2L53j3Q0YNzfZ5uCUWYJggbPP/8CWZ5zdJzw1s13MKJmcDQaLTxf8NqdAw5bB3TbTTYWVnhqc5OZKXgwTOjOXyXwTx/Al2WOrqY0Y8tEFFReie/HGFcySycMxgPKcox0gqKo8D2frMgoioxbd+8ynaagLeOkJC32iBttur02870uLz7/OL5n6R8Iup3Tn9RwDneiNXC2qmMSfZ/NlXmWl9ocH90gHyd4pgAKpNJc2FiGRsxAWpJQcaQtozRhkI5o2YggViipMErg+0GdQHVGEpaUApzEOHj8I5+g2Ztn69Y7ZONDpBC4cgJSIqREWUOVT8gTSZFM2RofvuerlO8kHG7fq9XiTqCkR6PRoNFeYDg4Ynvr+unH4+F58oG+658RSu1QdZZvbdpE/bmUoATokx2EFT+Mo3fuh3+xrnZ+s9SDTCugtHVbxSAoalvyD1zE69f/Ycxa/adDKodQ4Pke8QnfrtOKa5+S8jHyPCdLC2azhPs798nSjOJk5+0FPo9fucby8gLLywun/uzt7/1vVMbDqYj2/DLK5HTmN/CiLtlohNaGVqtFGMe0Wm2CKOZg9wH54DaddpeZDGk7SdzoMBiMSMuKye4D5O4WUkmWz11DRXOoM3rzAKFyLHVC9lptinAeT0koLaYyFGVC0GyCgklW8ubNu3xuOEEKRTP0KfMZt7fuEDUadEyJDiSFztAYhK+obElR6jMzFD1foTxBUZY02g0CK9BWIGXMwkKXG9ffwskQqQKKImeuG/PJF5/k+eefpttsUqQFValRspZ2CxXUTCfnkNKv+6h4SE7vtxXFDGv9k7Bk9Z5i00iJdo6ZGOHKnMk0J80ySmMQpUXpEmsryjKlKEqUdPjC4Ym6hSL9EBWEOAx5WZCewRoqdMH+wT5ISXTvPr5qYIqS/sEMa1OWF1okWcbm5nnevn6D7twcQtS5qMura1y+9DhRFLD94Caem/HjP/bjrPba5MMhN9+9y/zSRaJogSA6Td8Mo8mIVKe0l1vcO0oodYYfhjSiAFNqpAbfCpRQ6KLisH/E4fGY/cEAtiVWOxp+QJJVdOZ6vPixF+h2ewSeYHWxw3A0otVu0W6dvo7qREnrnMOZoh4+l4rFlYv83Gc/Tv/rb7DxkUuUccDYNxyFhoHIGYxS9vemDAeWUlu0D81OExkopFI04giyOspKypplciqcQwqFQ9CcW+WcUDin6N+/QT7xYFrHq9VnmcVVOQcHOyciMoWl7i5gNFgJThIEIUvnzrN2/gqN9gpllWDln8BQiKEV6Kre/UYnF7QQNbfSw+Gj6ruUVDgczjnMyQF1rk7j0QKcFGhdc0YrLLUbiyJ3jsooqjMu0vr13HvmRv/vUGP1QxMkUX89ihSgoBniOCmKDp577kr90x6+hHgoFnG12OMUtOdWCOIOImzR7m7Uv7cpqQ4f4NucSxc365talXM0mhEvXWV/ZFhdOUeeF4hAELQWcdayeCnACY96wqAwtiQdH5BMjkkGW2cfD+XRXdlgpYoR7R6Vq2PyqjSntAati9rnoqiNrP7r3/xtOmHEpz/9KXS4zPfefI3O/CEffewCYdOn3WyzuigQskWz0WRhbhXfO33afnywT7fXI9GO8WxGux0Seg3u37qBUJYoUqRK0D8eUKQFcRDya//6r9VqOQvC+ighkUpirUNYgbSy9ixRiqowQH5miHWdtGKw1ifwA+QJ318CQilub93DVUcUWf2EAQ6tjyiMoyjrrNFQWeJI4fsgpCKOWyip0FVFng/xvAClzlBKOkPmFOfPP4kXzBFGHQoPrrzwJMPhDnfe/Ec4HH/1P/1rtLsLtJtNfvWXfonvvfkuftBAOEEUh5xbO8/LV88RNjwmxnD3YMYb+5ql5WcRwj/zySR3hrnLV/BDSfrFV8mSCs9mdLttjvfGeCIi8iLa3S7OOoaThKqyLM7Nc355g4sXNsAWNFsxFy5dIlQ+STplZ/c+NweGxy49xbnV9TPdJa21731gNTiodMmXv/xNfucL32ClysnfPUAbga0MwoBUbVToIWOLCB3K9wlVRFv4eCctszzLMUaSl1OkVyDPUDgL5de6ASHxiYjCmM7cClefeZk8SzjYv0v/3nX277yJqKlztS8+DmNKrDHvpYAJXeGcJIyX6HW7hI0uwcImrSA404bgIT7UIp44hxGy3t2c9B2FrP11jagVbMIBUpwURlfnJUNdTK2jEmCdwLp6VKRxGFcH+2bWYbQlP8u3mh9po3wA/NMEIn+QtHvPJFSjMcPxjAfyu7RaCywsrZNnQ2K/Tjgqy4yw28ajINQDQi9m78Ee2kHpIoQn8aMIVzm0ztDGoU1JZSqs1vhho/Z9PgPnH7vG537+VxFf+R6ZcTS7bTqtBslogsbD8wWT2QxnPBrtgFhYlJM0mk1yOny6c4nN8+eZiz3asaAxt0FnnHOBgKvPPsby0tKZpv+z8ZQ0SVlf36AqKw4OcsbjQ3Z3jokjjzBW7O0NKHWtnuy2Gmhb4SlF6If1huBkN+V58sSG1YF3YmTlKoR0Z/uan7zdxto66OMk9s1aMDjysqBME7BQlAVGa/wgZJZrZpnBVJZOUyEwRL4HwpGkY4y2OOtoNXtI4dc3n1NgnGR+6RxBY5G5lXVwHjYpCRotoqrD8WhAu9UgirvkeogxGlMtE8dt0rxEOEt1Mp84miTkIqTTabA/LqjUHIH0ajXqGaI0fIkLJIUzFLljPMhQbUmZl1gL42mGK0qSzNCMY6azhMcuX6TTbvD0E9dYWFrAuDplSgAYQxiELMwvIIRifn6JsBGT5qeLWx7uwqWU2BOfUgE0ncaXmrJQ2JlF+nXIiVICESrcydZaocATWN9SSLDC4Rl38n7XGy+cwejTb2pBGKKrqh52I7E4oqim3zaaLbIi5Wh/j5IQq1O0kbVPvhAndUHUvuJSU3dmFEmZcP3WTZZyxfMbT1KUFVl+ur/6Q3y4g01rAUVoBZ6rH1OFEyhRe4HLk4sFd1LEcT/MITyp5NpJrJN1bxxH5RzG1X3x9ITfW3F2gX7opXEa/p92pP9/4Jw7swfsWufxpMfqkiRPM5QK0KWjTDNkFNDtdchsreiMlI/fgnarw+0i5/Bgn7IcUxUaL2rh+T2kcHhezbMHOL7/DlmRcFb4p3MWL4hZPneey4/N2N7ZpxG1uLSxgVs17PWHtNoNZkmKwAdlOLe6yL3b92h1e3x0eYPpKOF4MCYtBWEcsby5geikpIXh0tXnaDX8mhJ2ClpxyOHRgB2zw2CasX845niYM5vmBIGHUuCcoqoqPGE4t7JM1Ah/2G6ztr7ArSMIPZw2OEGturUW6Xs4Z1BnFC3tbG2jisN5Xq0UdLWFMtahtaGqBEJUoOrZzHiUUxlJWYI+sYAoS4FzmsAD4VwdWiIEgV/Hx+kz6PtCBXR7K/Tm1tEiwFpHZ24OT0nkpB6ulbrAFRk2N5gqZyvyCaI5skJjja5bSEIynKXkvmNSVYwL8BtLSE+CE6gzfComszGl8dBVQZUVZCNJ6TRVUpLNZpSlIW618P0Y5UVEoebc+gZhqJhfnKfd6eEFAVrXmZTCt/hRQLM7j5IhVtS+R9VZ/TZXb+Ssq0OZOYm2Vcoh0LhY4YX104/vgZIOqwwaW5u6eQHOE+ApnKp9WJyUJ+6fIFXdXjkr0MWdDLnrJTmk8hCyHrwqHJ25RS5eexpnKnbvXSebDVEuxxqN53lEcYx1FuUydFWBF2NUTHdpk6XVdaqyIApbFOUHS/YR/zQF6hEe4REe4RH+ZOCD9xQe4REe4REe4U8cHhXxR3iER3iEP8V4VMQf4REe4RH+FONREX+ER3iER/hTjEdF/BEe4REe4U8xPlSK4d/4O/+Dc9YipeDShY0TjriofWzeh9Zj7UkA8I98+SHfUgDSSawF7Rx5lVFqi3WCP/+v/Jn35Qm1PeVC38cLPPI0fy9xxgiBshYV+gjtCBo+iAxP1GkwSWFry0hX4MsAoZo1HU2AtQKch5SCspohbUWWa44m768fffudY1d7TFtWVrtU2pIXmqKsqLRGSv89kVNZlnU6jZQEYUBZlrVHd1lSFiVVVdFsNblw7hyNRsTR0YDjwSHr5zZpNDpc2vTfdx2/+Gv/sau504IgDFCerFOSlIcvFY1A0G4BTlDkBmclNhtQJDsIM+Jy85hr7YI8L3kjW+M4tTivy8L8BZpRj8MsJS0FVan5zd/8L953Hb/xn/2rzjmw1nA8HCNixY0bezQDn821efb3c2ZpTlrkZBbCZpMyTYi8iOXlHrpMaTcjsixjtz9ha/eQJC9pxz4L3TbDccbxMKWoYHdv/33X8efWn3Q7kz431YjNVHDc9DgIfEwlEdpgjQLh0M7x5Mc+zp/7C/82z1x7jEYQEvsegax5wDjJcNDn3l6f3/79L/HVP/wig9kQ6SyysNiyQmeT913Hf/BX/qJD+TSiLf7gt9/g8KikGXo88/gaP/1Tn+Bnf+7n0EaDJwn8gO3tff7e//wPQIa0O228GIbpmO07h+jDEbGyBC0PqyRZpvFxnH/8o6ytbfLv/qW/+r7reDApXFWVJMmYdDYjDFv05hex9clJVSZorYmiBkJ4tcpaQBz6/P3f+Jv8j7/xt4jjmL/yN/8uL/zE5whDwW/8jb/GP/zv/htarS5f+N5NilLjdM619d77ruM/+tt/272nsP5RnYerP3cOnBC4E863c+/DFnS8R2Gu/597z374YV3563/5L77vOv76f/KX3eXLF2k0BW++8Sr3Hxxx7/4BD7YHHBzOSNIBQsL8wiK+FxKHgtWliLW1FXDQ7/fZ2TlgOEopjAQkjTik3Woxm0x48aPXeP75T/B7f/Alvvm9V88UqXyoRXxpfg4hJErVKk1x4mB4Gl9bSvme2OchfjSF3CCxCpzVKCvxqP1TTkNhLEpZPCdQvo+SEqEtnrEYZ3FFRRD4+EpgrIfwSpx1eK5OtBbSIZRECg+ldB0tpwVVBdYzYAMsguoMMUeWpZRlnQrT3zumtJBWdZSTrxRZPsYYTRyEdYyVtcyyDPKCIs8pCo1SHmHDJ3DQabeJ4hgBzGYzykojAHWGIdhDc6daEONOjnmdRu60IZABuvJqm2BrCDwoyyPI+rR6IQvzDS6vd3BFxlzp8+r2lH5Z4Qc+Qsb4AcRSEKrTRRTGaHw/IM8zlGySJjm7O0M2lpeYTUsWFtp05toMJwn9ccrO3iHdwGf13CJlUjCe5kymjizLGE0nNNstrMr/b+beLNjO7DzPe9Za/7jnvc884GBGA2iggZ7YA9lNSaRsUVJk0bFVlpXYlRu7UuVy7FIl5cqFnaiS0oUuMlRUiS2XIzmVipOKqFA02RwkUhybZLPZ6AHdABrzmad99vjPa61c/ActUnLvowsX5e/moLA3Tn34h2990/u+NJo1lIIwDJiSLjt7k8UHCt9nauUMtzdvcapTR2cDVqOIQrm4lpJCGZBewAsf+xiznQ71MMCVisBzkRaUUCjHxZ2dYqXZ5u9OLTO/OMNn/vAzjIZDpMrJismyebOn5+jMTbF+fbXUkZUAliB0qderOL5CFwLlOQjpYIUgiRPWtjaRQhJWS6j73vaAhpSgLKO0QHkuDg4q0ORpSm4m74m3HcWt+7f5zP/3r9nZX2N25jQf/6mf58y5xxkO+/z+Z/4lW9urnDp5ho+//GmWl07T63c52Bpw4+3XiNOE/jhh/cEdXn/jDf7jX/qPWL1zh944RiN44/XPUm/M8ZVXvsFv/tP/+kP9cEQZdB+tiH9golwYt9hDNqbDTz8kDNh/x99b+AAd/md++5+zu2s97q79ACk1nkpwXMGJ403mZgN6g5R7D2sMRwk6s2zvbCGAwUHA7vaYdrNBp93m8sVlbt+5RxTHCAzNRsjVp5/k2pvvMhqnROmQ+aWjBc7hJxzEg8BFComjJAJbEgOVYOaJ/+7Rp4e7/eXNEqBMSQsfZiOC+/fYPXkObe0RtwB8Kan4AcpzSbO85NlwnRJCnVmE52CUpSgiEBZjFTZXOE5S8hkISZ5nCJuihUIgSaMY1/NQyiPpdXFccI4Ac8zMTJdKRcZSq4SMc+h3Iw72R2TJmNmOj+s7jKIIOxpjrWUYxSR5QX7od71e+yDwjqKYzc1tfM/B931mF2YoCoM8AqFolcRIQeD7pTq7VGRFUhI2eQ5GOYwz8EVGzc1w8i4Vr8v8SgMvVDQWjrHpgMn6dMR7/PVPrDB75af4o28PeetGTKNRIc9zsJNBNpsbDxkNx2ijWVmcpzPX5KlLC8x26sxOt8hRJFlGvakQxCy2F5H4+IHC9Q0EmvcfbOM4DplRDKMMJUuWvplOi/3998lzw1R7Mpw5+hsvU6tP0f58wDvvvsEgGmMCB2FUGS/QFDjMzC/y4kdfYnFmmhCNKxWhUgjhoAx4VqFlCTBZnG6w9Cv/KVcvX+Ef/8P/gtFghDgCo/Hal77Kz/3KKZZP7fH0x0K++RWL73scP7HC4vIS+91dkkKgVFBWL90uO5tdVlf3SeOS96akRZUQumghSZVDpeIw3wmYmgdjVhmN6hP9+Hv/+OPcu3FAnhhcH/L8Tf7ka5/hYH9MnipqDY9arcLanTt8+ZXfZxT1aLY86rWQ/u6YZujTCCr87v/wmwxSyx/93v/KdCioKYkbKP757/w6YcPBFSHw4UHcU49eKPFnQkZJYW1NmXrbH/nwKByMLSlSsZaSl+kQlDXJ/FobbQqMKYgLg84SHATHFjtcOVPBXf19Oot16qcWKTwfv9bg9p2HgOXY8hxzMzO89vpbjLOI7qCPEC69Ucwg+gaPnz9FmsKXvvBVNnf/A0RsCilKGaNDZOajsqtUtPwQhKT40x9/elAKjDBIK8uy7eH7NL/yb9n7z/8JoigQYvJN8A5paItCI4qcIi8OaUJBKYXJCrRycDxRZlWOwBYRtgDcsvyRyiXPU4SoIKxGSVGixrRCGLAFqCNGDoXNkK6BQrHfH+D4IbOdKtKBtKhyvOMyTjWr/V3GUURRFGhRECcFRQa+D1k2oupViKMI67q4toI1lla7BlKQZgWpf8RpYgVSOgihsEgy61IIW+pMSoGSBs+k2HzAeHyAU/RYnq2R6phKvUZUaEaDCD0c0plfpLV0GS9cxNp7YJMSZactRx2vp08uobUmyxKyJOfe3Ydsbu/himlmpxrsbG9RrVaYb7cJxBzjKMb1FFJ69Ae7vPjkYzx5/ixFnvHF771F794mnUqdl545z6kTC6RJl4PuCP8IQrCp6Xn89jTtk8e4+4NvkTqi5MkwEmksWmq0VTRbHVqtdkmwpYsSuUf5nBoBWmrC3GKEwLqCmgg4Pj3P4tw8W0IQ9SdXBGu7Y/p7PZrTKbeua4zRSAlJWtDt9hlFI6xwqdQ6+JUQ1/MxSKw2SGEwwiCsQFqBMgJjDXmRMRaCcSyojl2Un1OM9yf6cefOOkElZGG5xoM7u2yspriuwziKkMLDDwXKzag3DU3RZHQrRhYh1mi8UDLdDJhyPfqpILcZnUDws48vcvVYk4c2YT0C1wU/nPycqkdskuKQQOoQKS/QWPEovfvxPNo+egdtmfjZQ24jYQ3ig2+Wf8qFpSThmfycNmrVUp0pL+gnXTyd0/ZdpmWOSQa88Au/RM23LD52qtRZjUesrq1y69Ydnrhyguc++jSjKKMz1eIH125w7+4aAo2joNAeJ4+tIEWFRmcygd4j+8kG8UfaheLHT8oPa6d82GfSWmShyJUgSA/ovHUNf9DFyFJW4ohuCkEYYq1BIXERWFme3soBx5HESYbRFlcIBBahAWGxh9wcxkp0LnGlW4rOSsjjAls4CAXCkRgjjoR3DwcRyrFIAnb2ulSqVeq1GhUHZjoN8mRImuUUecxo1GU0HCIchdYKaxyElRhfUQnDskQXAuF6pEaz1x8SxzGOdPAcCXw4Q5y1ZWtIa3Acl1yD64cox+Aog6PHFONtdDpAFkOESkmzEOW7tKanCCoB/b09sJLjV64Q6YBvfeFN3r8XAW2yzGCMPFK9++HDHWbnO7iBS5L0SdM+Vqc4UpGmCft7u2BmKVLw/SqjXpfFpVk2NzaJs4TxYIjOLa4neOmZ01w6NU/cH9Hde0AWb/LslTOAPDI7mzMBOCHTp5bZCTwORA4IXFMGjwKLchSXnrhCJazgYFDCIKzBaI3jOBglSJUmzAsK4ZA5Fs8oZmptXvroy3z3h69x4523JvrRnj7D/t4B1VrGcACeKwHD9k6X69dvU60oavUG03Ow0FhhenaWsFalHngUDsQUmMLgOoqq66KzDGty4hT6A1BeSliReGKyyvzyXBPpGnIzYmrJQ1uQFjpFgzx3QOQMBn3aUyF5NqTRNignwRYhg90UNxO0qjDjC061W4SywJeSj5xepHrQ463VderVKtERcnWSMiaUebf5s9GaUtvK/vm63v5pcH906wWPqLAfHQygrDn81uTKtdmsURQFWZ4xP12l7jvk0ZCw2sC4VRIM79y+yer1m5w+sYzrufzCL36C5/ef5cqT51hf3eKVL3+NJ568yMsffQ7fcblx8xZSSRaX5lk5tUS1Uy3nbH8B+0sL4kJ8eJD+cWrY0n70xStPVIlVmuDhHSqrd8kDF2UVRspDTewJfliLsBrPd8kA4aqSeVAZjGMJfUUgFb4x5BhkDpnjIpUGaT8gpncAJQ3WZEitAYWxY4QnyBOJKSbzRRst0FqXQqmFQSUpvutQr1VoeHD93gbjJGY02CceHpBGEXluCcMmflijEtaZnpoh8ANmZ2eRSpFqQ5JnRP0xVheEnkcUTWbLs4f8GY7jgBUErsSRGsdkVJQh7m8gkj1cVWD0ENcrCchmZxdYOn6ci5cvEQ3HpMMx33/1e7z17kMGSYBfn8cJFblVeO7RnBRZLhmNEvzAEFQk588ukx+XuE4F5SiOHz/FeJCjtaTX7TI/PU2zWqVyokZWaMbDEYPhAVNTDWYaAcvtFuOowu7eAYHv06oLtM4O9XU+3Kp+SCQlypEsOwKRW7aNIT4c+DpWMDM9wyd+6uMoKJV76gopLUKoQ7JbTW4E2pYEScJkWA1SauaOr2Bv3EBVJlOvPvb4i8zOrlILbiDFPQCkkmxt77O32+XMyTmmp6aoVmtUfRe/UicMfYIgwBYGoaFQGtd3CXxFagSucbDKIQcOegVRAlpP7s1Lt8HBdkqWZrQWDOcv1liamyV0HXb3R+Bp+oMhyslwvQwhcxzPIIqUpi6QoiBwFAtNn6Xpss8rPIFwJK2gUgqea5f9fjzZj0MlJIHA2kOuUCHQyDImHC5JSMohfWnmMLj/aQDn8LfYD/6dLRNDDFZYjigYCQKPXAuENAyGA/aGlr3dPZJ4lfv373P3/l3iNMfmBQuzDX7tVz/NsaUZptpTrN7dZP9gyOLSAkmSMu5tcuL4Cml6QCV0uXbtHW7cvvNj7JlH2V9iJm7/HJPghw44f0RxHCgZD5WlFQ+ZunaTLIopnrmIMOXvPKIaoig0nrRcrdXZMobIcYlyw5iMQGieuHCBVn2Kmo0YpCnbWvD+9gOEyJC6QJgCowqKPEM4IabQOI6LkYIkyQ57P+KQAW/y9XAcByUcvLCK7ykatQqtWkmQs7u7gTUxadwHHRO4hqrrIUVElkYUgcXzlpFK4XkeQkpwchzXQ1CQRNBsNsq++QRz/ZA000jl4bguwmpMppHCYIsxWX8DUYwQPgiZovwqwyRnyRiq1oBQzJ46h4vg85/7Mgfjgkqtge/XkV4NUxQ4jigHwBOsFrjYIscWFi8IcYUCR2KFh/JdstxD6yFJOqbdqjE3N40QmjSL0UXGdKfG4kIdKWVJASssvh8wM32GPNdYM8IYQXYEj3dMQbdahOCJAAAgAElEQVTfRWU5U0bT0aC04bZboH2X6thy+tgxfEcRJyNqvsvDtVvMzy/TqbWRSKQwCONQCI3ROVIUGFsgPMPc8eOcvPD4kQRpzakpTjy2jDItXG+XNNJIAXmuGSSavf0BxkiWlobEoz5RnDIcxVhr8V2FWwsoLHihRzP0iDynlG1zPTCGYX/IOClI89FEP2YXX6Za7ZHEu4T1Nwm9GklaJ0ldjMhRdr/cxhGaWlPgeC4mDcmHMXOuoD7lc3y2g+9oVs6fZvPWPXJhyfOcaQXnTtUI6wbHmTxgVcI8WkH58Y0UKRDGlERalD3tR6Hk0ebJ4cz+g5/2A1Z5UyZ9h5s2j7ZaJpnjlLqdjiP44je/zd7+PnF/iEkzlCNL5fpc4yqH7kGXnf0ut2/f5sqF82xvbKKV5OLjp5BSMeiNuXN3ldmFBVwhePhwTHawT6fTpt2ZLLT+gT9/oW/9ezL1o/1v+JGX+oNOYtmPekRcaA+DPgIpJIUsP3O0wB/vUHnvOvO/8kmK9+e5s5WW5aywRyV8WGv5+Nwc/+D0LP/svTFaJ3ysVeGlRhV/bolvX/gEfeOwtr2O57hcXjjJxwLJd773J7z53uvUqgpnmGGUwQqBUG55U32XmvSIxgOsKciOOE3mFxsIAcNBzP3diLBdxVrNxuYWvVHG6699i6z3kOm5NucfO4fIC6qhx8bOKhvrO7i1ORbmZ3Ebc+Ta0ml2qIUCXaTkzTrauCRJxhHi7hgrUE7ZD0/TnIrrEnoWm3QZj9fwRJ+pTpVcRwT1KgZBIQKSKMfdX6N4bx8e/whR40l+6lN/jVf+4Ov4/hxaVkmMADTy8AWbZKdPTJGmSamc4ng40uIqEI5HqhXf++5bnD5W5cKlZaJcIAPFeDik1g6ZCVpIqRkPe1hcClPH6BQhLK4vsHnBOIspclByck/88//n77K+sY0h5QXhc8J6fFwWLBVj3s4tV4I287WArf46cVCw8e5NluQd1sMW+8MGx849yYVLF2hWqlil0I+oR1VB6LicO3WC1Fr+33u3J/rRW/tt1PmfJq1d5lO/9ve5d+82W/dugR7ikXLj7i6tMEJZzcbaBhtbA+7e2qBT8Vjq+LRbFTKqJFLiVgzioIYdFuQChBHsbPfIspxeb/LW0M//lCAr6jhiltVvWEYbYx6sr6HjEZ98/jKVl16mMBoj8lLjwOQ4RYt//TufAy/lxLSLSSO8aovbb15DJDmNqYD1/ZgD6rxw+QVmTs6THHGYuFb/aUUu5AdxItSGmtDkRjJClv3xDwKyRkqFkIIizT5YAsiNQWGpCWhKQYRk2xSH7ZojFi2kQgoFUqCt5qPPPMfZ02e5ceMm/d4+3/vB9yAvxUFOLCyTpZJKfYlKY5rRvVU+84dfojBw+YlLDHe3uXruJJu3u+yPY1aOVdntSuamWvyz3/jNiX48sp9sJm5L5R6LPYzVmkdMv0KUJ+GP9SutPDx4LebwFHaMQEtBZzTm2NVnkb5h+4ufxzz9c480f/4CfkDLxuT9Xf7OfIfdJGHec1mqhaxqxbH3r7Exd5Z+tMdsrU5dx+SyxqUrH2Wn36fXfVhy0huN45cPCKKkw8zzMrBLT5X8uBOs2+0R+CF5ZujUKwS+xyCKKQpN4Du8e+sO1179JudPz3FqappnLlwkxfDFz3+er37nbS595KNcfuJpNjbvU+8sl7qDhUKJHM/RVHwfaS3eEST39Xr9g+vuuwFN38djxP5gH5Pv4YmEqfYMSA/hSpK0wOCy1z3gZgDVSkhl/V08f4Vac4pWZ5nR0EULDys1UjhkmcZTk18OJ/DJTI6VFl1E5IUlEQIlUzZ2hgxGgtl2m9C1JKbMpaqeSyVwSNEkSVrKsLkKVxjSXNMbDNB1fdjzBIRh2JvcA157/xYm0xSOYc0K6splodAsA4lQnHF86p5H1N/j+2+/zvde+SP+4S+fpz5leP21t/nG6+/wc7/4KV548irN1iwohbYShUQIizUak6dcvHRxoh+5s8bm3iYLiy8yu3yC5uxxDk6cp7d7n2S8TXTwHTxjGRyMSOOU++s9bG6Qj1ZllcJiMDZn96Bg3LOMhgmpyfE9HyW9skpQkwfwW+s3GGQJrnYZbfa4cvwYL1xaolKpInXCO69/l42OwIgCEBgpqAVLeG3Frbf71GWNqsjxKpbxwQE1t8LyzDy7RNy40eXy1Qwrhkgm84mjD+cOWByrEcpBWEFVOASH777SHG6MAQJ8BfW5ebqDmGFvD8ekzDYDUh3hOwJPuKSFJdYaR7ooYTFmcrKxuf6QIkswSjAeRczMzXHuzDnOnjwDjuD1t36IpyQmz/BdhyiKKHTE+ppPnpXPoqMsnisRjqBQkLsSzxjUfgKDmG27TjKOJl+PQ/vJqt1/EKDNoTybLsWFedSv+tF1Q4FAHq7+aKzQOFogrMVRgrAmmb10ntXf/W1UrDFTM6W+z4+2wz7EpLUEnscPo4IXheaSZ4nDUkasHg+It7fQsiCxCjOM6AW77Pf2qLamOf/E07z1bsyw2ET1QamytyakxZiMoih3t7UG74hyeTTKGQwKlPLwPJ8811SqAZ7no/OC+fl5xqMxgZVEvQHbOzuIShVHueR5TJYf8K1XP8fDtXuceuxxTp1+ilbjJK7ycaVgulFm9lrmwIeXZkoprDEIWQKnTKFJ8j6jwSadakHTCbA2o1YJKIxG+D7T7Sm8SoObq5vU6zmhd5fAvIrTehbHqyOkQFtR3mkr0UWO4egen3IchDCkSYwxBiEkhbDcuHGLh+uS69d9phdPk6VDhplA6hQBpECWRTgY0jhF+B55kSEE5HlKEIaEfhWBwFWTB2iukGg0Rgi2lEBlGYESVBGcNIrpPMcZj9DDIQ/eu864f8CDBxvMF4qttYe8db/LYHxAoMe8+NJfQToVkC5IARryNEZnMWceOzPRj6npJoVxuPX6DiiPRqtF49xF5paXScc7+FJhx10aMwtUqg3k9A7D/DXajZCg6hDngvax0yhdcPO7r6MKlzROSHSBaFRwAh+VJ8y1WxP9OHP8ZTJpSYcjXnvl/0FMVbny5PM0Hr/CwaDLu//8bU7NXqBz+QqFUJgoJl/dYzwz5mb0kPf3YmbdjHbbYKSP8kLGSQxS06pJTi4voxxD1Z0sGrL74E65quq7NIKAcZZjMk1VSJQE5XskRYG2HKrnCJYXFjlz7hzf+P47FLJKHo8Yb98lKPaRQhPhkmpVTmqLcuZhsmSiH1/8wu/TaTbZ66XMzyxy8tgKp1aWSZOUb772fS5cvMjB1jbD/gGtdps4jul2R1QdSSWoYo0kzcpkrchzDILm9CzDyFAMB4iRpjfaY/3+PXjppYm+wF9CELfWlqW1NFhbvqRQitjqovigH/VIxaNEbILQksJKclVQEdD48rfYL2Lk3Vs4qoGuNEpgizyqGAJPSiyKN2dPcoV9ssEOXjTGcSBUEdnUNC9sr/K4CGkXMe3RHa6nPpHvsTh9HKaXeGgSNtMdMBnSOIcIvcNpd66RRUlWP8mM8EiyDGMyrATXk8g8J0tSPM9nPOzTrIa0a3XWV9d4/YevEXZmUTjUGxUGo21u3dqn1xtyMNxlfWOTlz/+a0x3TpLlMEoAK+kNJ5ep2ggqnoPruPQii7YGRZ/pNtSVy/LCHIPBkMJA4DjUQpfCGFwJ1cDh9oNdOqFHLbrFTnuF/miE8lqorBRadvyQTAB2cvDMs7yUL0sj6vUaWmuEKIe/zz/3JK9f/y5vvbvG2cvLmEDSi2IWZ9rkGrywzHJCXyHyAs9zwWg6nYVSxKHIqVRqJElCEB7BNmGhEJAJSS4sq0LTkIplpWglmkaR0X24ytQTFzg/v0iQ5mi3xvt31ohHI/JoxOrdWzy4/RbPPP0RwpqDUg4CgTGGaDwkcBWN6ckrZPPtBW6/u8Gt97/E2UsvMn/8NDNzC1SrLQJPcubyC+TRCIIAIR3OzpxjdnEJv9rAd11G4zHSqdDG8Ma3f4grJSOtEUrhVadwXAcpYfHY9EQ/RgfrpMLS396j0GMerG9zar3LMH8Xp9nmwuUnee3N77NvBnQHBf76NvNpju4bdJGzM4TC0SwOY2YqHs1aQDEe4uaC+cDhzdffoHU8RIvJA9YiGzIzt0i1Pc0gheHWNv1hH53FOFIQ+j6OzXFVuTCRZRnSrXM21QjlMswNzXqL7sNbdBgQWRiNxxht8CsOB90YayRKHzFgNTnzCzOM8wG//uv/JafOLLM8t8DB7gHPuw4Xr15m4959vv61L3N8foFe74BkFNFq1nA8Rb1RpUGFRqNKFtcZjGNq1Q660WB3f4skg9zRKG/yjOCR/USD+COzWKQUzM8vUq2GSFvqURaFJooixuMxRV6AEKXArAFwMUpSNWOuf+/rfPMHr3FcDGnlhtrTz1E5vkx3bMjyjPQIeaXQdVA259TLP0P8J/8GG7Qo4gRH58ikx6JOWHR83un3+ezmgMcCyd+6eIKuNKSbbzMQlg1hueUVPDSSDXO4CyxKOTBty3a/OAKxmWlNmucUhSXOUlxXkmRl60NbxdbmBoEr6e7v8fZoj72DXTLxgNnOLJ4bAILd7T7RwKKiA0bDWzz99Ihj81XG8ZissBRaEceTB3lSCGxhkK5ASSjyITrd4/TyFHl/VGp/Kpd6s0XdlzQade5u9hhsb9MIFQ/X9nnQbHDq5Aw3b25SGIUmQzrgyDJ2+76HPEL4NUkTNrc2cBxJvV5DqUMVH9dhNqzx+NVZ3vr2Gp/5/GskMsZ3K5w7sQzWIIxhdqrKhbOLhNWQJB6h84zBUON5Hq7rkqUaY0q1nUlmUWRKk8myxE1ch/s5RGnBJasJhcHsd9FrW8xVAvLZGb749VdZ6vgcWz7Lu/f3CJTl7PF5xv09fL+K6/g8qtKtKZhqt2g0J2fA457kvTe22NqGZnMepSRWp7TabQQSIxQynGM42sP1BGHg89jF04xGY/Z6KSvnnyIajXjz2qtMdwKKOMFVPo3ODKo+Rz72CQKXWmuyH++sv0FuBOxlREnK/Thif6QpTI8WVc48foXvf/+HvPetmwz7EStSIANJ7lTLNkUB3dywejBipdXGdUoZxXLlUaMDl0KW+IpJpmZP8d5uj+H9u2zs9UkKTZbkpHkKwlJzfDxR4DrlwoA2BrFxl2sH/zc4IVlm8b2cYislEB7SrzI/cxJPWcYiJxIACmUnK+qMRhnX37vDyYVzNDKD3Oqzsz1AeB4zlQYLi4scX17h3LlTjA66rD24x9nzJ6nVffIk5m/+8qcosgzlCrqdKgf9AVtbmyR7PayCQhmEhPeuv8Nfm3xJgJ+0xmY6RAqFpxQf/ejzxHHC+voW7XaTlaU5HGGxQiJkOdA0tpRRS9OUsFkh+f4PWP9ffpuP7D7kfwoyfuvffoEi0fzi31rm+Poqdb9RCqEGk3vAaZrQOHWVmaWLvHPnDlkhOBsYmhWJNYo011iv4H8bK07+7C+xX/Xw/vjf4F1+Dv+lX6DxzqvMvvFdXpxq0xvss01BZWGGzdzyhfU9bngORkB+xETxc698FhDkeYERFYLAxfMd8iLG9QyekFR8l/WDXfIkwlECZMF6XuDWmwwOYroHA9IoZ6bqYtIdvvrF/5k3av8XZBmDLAGvQxSN+dVf/NyH+mGTIQMrSQpN29dUnQ0cJ0JHmqX5JRI7olqtYI3B90KiccROt4uQCic3LEzPIdxZvvzqGnF9ASV9HCXxfUlRRPhuOfi1TD7UAt9haWEBITmkNHBQ0sNicZTHL/3M45xbmuatt7a5dTcmc+Gba9dJraDuVpipGhbaNVYqHfb6I4LQI46H5AVgBe3mPIWJiePJIJtqTRIYSaZzxrElFy7bvkRnhquuh4xjmongwQ+vwQvPsRMrUqdK2GqyMm/5V//drzA11aG1sIIXCrY3blFpTlEPGyinwmJriliP8SdvfjLdeZJLlx6yOGuYbi8gRyP27l8HfQ7l+AjXI06GDPq7TDc7GC/k//gffwMhXZonnyXNXabmFrh09VnOBlu8f/MBmzsDUCFupcHUzCyVmiDNJs8I8Er0pxNCOpZsj0bcv/kez1w4za33DnjmY09y7qnHeOuzX6NhBe/1xriez/qw1Hx1QxdfeuQ5OFZSCXyyrKAtPMIgYFMUjHVGXExONkxtkcH6mG9/9Zv4rmWUpBSpJjMOWpRamgawP1KNCwPF9TugDUoXZGisdTFFzux0g1/+9KdQdQ9hLG5VgSgwR9Tyu72I+OEWTVPlM7/122SPLWNGA9TaFpUoZ3oYsRMPWFvb5uyLz3F/c42d9TV8z6NnU4bjCIlDvdPmgJj5M8cYDUaYNAKpaJyaZdA94Pd+71/xT37jv598b/gJB3HH5kRRTCZdrl9/l7W1DZRyqVTOYSgJhYq87CvneY5EoHVBlGbIbpfdP/h9/L1VTDLg5ZkFuvsjhiuz1GcW6FRbKDeg0Aajj9gRsoZgqs2xk4t8LYXPDQW/MFXh542G4Qg3D7i7nzF/4mmeuPoURZZjCo1+9y3kuVNMPfkUvcGAnfu3qSifJUeSDwY8Nd2iOl3hv9lLUIdAoUm2+uBOWTmkKVqAlBbXlURJDyVzKr6H47lE4zFaOmhj8d0ALXwcIRkNh8TjGLTHuFugyYiG77PjPcQzgjjNSAV47uSyzGhNgQBtydIBsthmYcohVC57e7ssrEyBiRiNhzRrLp12m3YvIS80VZFzevkkd9d63F/fo3E8wnNdhFeh149BgheYMsPXR7VTMoyxhH5IlhVkWYS1liAIiHRMGPqcXJmiWWuj03eZmVrEyoTba5tE/Zxmq4qrwBYFgR8gDodU2hikkKRZQhTF1GqT2xh+4JGO43J4ZkuQl0QxJRw6uGjfgOdy/Pw5rlvDXhShhYNTaSB9n0otYHFxEa89g/ECTH/I5tp9RrUWc/PHcZSi5tc4GA4mPx9bqxiRcvHiFR6sDZAqBTSjwWb5fzAug26XPE7Zje5SmBt4QQOhPBbmO2RJn437PZaPn+H2Rsqd3ZRRmlLzHQK3QZ6PKUxGlGwfeV8QpVp75Cqu7e5zfGOPi+dPsjI7BzanIRRD6/LDu7u0q1W2ewIlLSDZG0a4SjNXq5NZTSEtwpNQQJTl1A9AhxZTnXyqVW3ETKDpBJqgUsXkCdoxuMaghSCnJMtTstTKtMaQIfFEgnW9sm9uAJOjHYvjeSBUKbyuD/V8rS6JvSaY6yhENaS6NIeMXYpBhBsX1GSAf3yK5O59Zk8cp3swwAqBcR1szWNYaPakoXN+BdGLcUypN+wqRb1WZTyKyTLDw411qm7A0tLyRD8e2U80iM+3G6znPcZpwY133+P1H16j1Wzw3vXXuXjxEp/85CdpNuuEShGNxhRxQhhWSpHer7zC3re/wqAYsz5MeTUWzF59lquXLtOamWPj4Tozx05hjOaIYTuNhRp912eY5FxwLd92BINCo7MCI6Ca5CAdzl++zNTMMYp4TFb3aPYT0q//MfGFs1RbLfJGldH+PlN+yEDnHAzGnKq61AYZfUAWk090m2xQ9Tx0PiAb76HRJDYnzzIc6XPs2Fl2AomJxOHDZrCECFVBKQ8lMkJXozyHdt3H2AzpSDrNFq2whhQ5BZpKpTLRD+EF6DzHGBen7uONyi0Gf6qJVWNmKzWagUNuFJkekUrFiZNT3L29STWsonXM3v4qSnj4jsVVPsa45KJAU5AnUKs4aD0504qiFIvAUR6FLiiK4gPiL7eiGEc9jC6o1wKOHWsyHuyyMF8nzRR3hz06rRmMNRwMBnihT1FkpGlCID0q9QrdXh/fr6LN5AFakmYUWqOtRSGwGJSB2VzRNJaRYwmnpyiqFR5ur7O1t02Bg1fr0JhboJv5XOgcA69OXmgalYA8jRkMu1jlIvxpkJIjsGC8d+9dTL+CKE4ihKG7u8Ew7dMep9Tr00gTU20uohwY9fZxnZzTly7ieQ5SjBjtdLl3fx2RHjDKGoSdKq1AAS38sIkqRsTJkNFo8laII8r5lKgIPvLsOa6tfZObdzd4TX6fc8sLeDMf4Z1729zYHLCdCnKliXNL3VPMdqZIdEY18GhOt6BSZYxDYDV+GCKdgKC3SdKDoTc5HLkSsnSMWw/YiwyZX0fLlNyWRHjlUFwiRUnHIQHf8eiEmm7i4eiUQEqKvMAYTbMW0Kp4NMOy9ZKbEr0sjsAzuFbgCEkY5QSOZOQ7EFYRwOwzV+m9u8b4/j5ojSlyiFLcVFMYzeK5FUbKksUZKrFkSUKUxvhuQFirMtdcYO3Wu8yvnOCpp56c/IA8uj9/oW/9e7KluWl29vcZjgdE/W3OnT2FsJqD7hbf+JOv8KUvv8KzH3mOS5cu8fjFi0x3WiUniIm4ce3rvLW1xutGsieatJ5/gUHQ5NWv/DEXTp5gqj1LpAGT4cgc+OUP9UN1FDeHPZa6A841Hf6uJ4mLDG00nhAYR5A7DrMnzlNptDGuizw7R3TngGxzj1o8ZIREGWhIj6TIcUKPTEmi0Zhn6z5/spei3MmZhcp2CL0qwk9pyQDlgLUF1hoajSZWZVQbATt7ZQ9RCEFmEjpTNRYWKwhZoVapUqsEhBWHEuAl8ByXeljF5uUKZ1gJJ/rhVxRJlIORFIlGOS5R0ifb2WOh02R/e5Ozp2dozcyxM9wkdxPCWoXpJGS8nROvR4wGBtd6JMk2tpqgRYDBR+ChQpc0j7FHtJcEkjTNiEWCH3olcEsIlFJESUwUJ/iuT+ApVlamuX3rPjMzAdXmInlaMDNTJwh8lAu9fpcg8PF9H60NRkNYq4KR9IdHDNB0SYRkLDhKIYVBas2MCKhqTe459HoDeg/vMyoSbJGSpJqgPsWpS88y1/To5T6+62BNSrUSELjTjArDxu4+QbOCsiFKTn79+sM+6Y4HYhtlNFmS8eDBBvce7KDzjBc/8bO4SZ+97bt0Wh7Lx09ioyFbWxu0Oh18BcO6Zffh64SNp9BunUoYkCYVHHIKM2Y46HOwM3kbY5RkGKWQQvCpyycYb2zj1xbwKy0GvqAzNcOlx5/l5LU9+vl6OfMiRSiHVi0gDDpMt5qcWJ5l8dwKeX8bd7yGF1aJV7tUbIIq3CO3g5W0XHrsFMtzU7z5YJ/uMCHNMvJCg5C4rkvJUqse4e1otNq8/OQZvvb6bUyaIFyXcRSBMTQqAY4S2EO4fQnnL/fiJlnVDahMt1Dr68wu1WmmCmVzdBQT7vWIxwVx/wC3cChUufCgpWRpZZn9esidt9+iHdTw3IBjp4/TKzKeuHKVqXabbrfLm3fucvLsOartzuQLcmg/0SC+urGGFBn3799g/d4tnnzyaQ72uzz37FVajRq/8Zu/xTe+9W2mp6f465/+ND/3yZ/m2htvIMj4znde497Yst1qc/HSs+z2+7z76mvsbT9kZa6D5zn84f/+BaZaFf7qzzw/0Y/hoOD9W7dxv/xZ3h/5PBYaTtQcqhR4SUKWWIznEngeFZmzW0SM7vYY55pdJLWxJagaarMGdkoAWSEsVkoGWc6LyrKjFO8dUZadPNFBSkmSCGp+B0vJjCaVJaz5DMYpfuigLeTagrB4vqU9J2nPZoSeRz1UhL5FyhKogJVYU5DnKTozoAVxNLnnaTHlzmouKKIxmZPTaHfY299ma3cftyIg3eCpp89yYr5F5GdsFgOOn2vSVZDsxij9gKbMOdjOyao1gsYs9XAOD4fQ7uKKAu+IcrlSrSFVitaGOIkZj8copajVauwfHBBWKiTRmJm2R1it0pieoVqrUnUUj1+UbK9uEo3rVN0KCEOSjFGq5LceDiMqjTpRMjySiOsRA4dFgFIIU2bkYbWKSGNEGiFNStXA6akOu4M+iRLceP8OLz7/DI1mi/X9mJWKgyNy4miIKXK0EFQrDuub90lSyc7uLp/+xMc/1A+Z+wz7kmG2RtTvMjUzTY5ifNDH5GPOXf04N7//NUa9feZacyS9ATur99jf2WXQ7ZXkcFKzszdgYSZg2K8wjsdIGVHoLVAJHi5nllYmXg8tBDovkNZy72CTv/mzL3Ps8avso6hUp3CSAy6ddPn7f+Uj/NPf/Qz3+hGOUviOQ2804ubDVVzP48b9OniK40uLtOsVdvf7jAZrTDuaIIox46P2ygwznQZTjZA8bLLRjUjyAqNLAI+UqmRtFBxuvVnmOh0+cuUx1rsJaZxglUOcJAgLvqdAKXKrwZagN2P/Hdwrf8aeeOZZHjt/jkUZ0ru7Sr1dx4zHjIqC8SvfYlpqIgVVpXA9Sa5T0jRFNCvs7e8hhOQgGnP6ybP8/Cc+xptvvYnve2xt7/LgwRqteoNjKyeodWaO8KS0n2gQ/9o3vsrc/AxzM022HsD7N9/jYH+PmqdZWZpneqqNES6zs9O8+r3v8NnP/gEb62u4rkMt8Dn/0s/Q8h2OLS1wvh6yv3qdQDeJBn10ntKsK86d6LAwPbkHnEeCaqvN/fff55XNISdqDo9VHE6qgsdch/OeRh8OR8JKSLHfxY5jAq1pCclI+uykFjcT1I1BGoMROeQFQa2Cn6Y8VZPcjyb3gL1AkKYpyJyh2UFKgxUFRa7pDQTkZRZoERSHXBFhpUaz1SIIciqeh0Kg05zCgtYWYb1yRc4WIEpeFn3ElowxJduHqywOCdpYqo02ynfZXr1Pa+UE44NV7t/e4kJlhVpYwRb7pOOELPLobt7n1NyYVliw1cs4KBTJ6ABnsItT9MjziEIa7BHbGEFYMvFtbW7ieiW/jjjs6VsMSjmMhhGxn6ILhZKQZRlFapidaRD3e2zv7nFm6hRKSZQsWQULXWAspFmGVHAUJUWmNdIKkIocibAu0lo2dMacKFiRCs916CURZ2aX0dLwhWu3uX/nNv/id/4ljXqVSxcu8Hf+9q/ikx9CsBV5MkbHKdd+8F1uvb/Gfm/Mf/tf/fqH35e0hhsEVDoNLl86RVCpcuf2Te6912N6dpq4u2Y9CnEAACAASURBVEmeZ9y/u87Ow1WazRq9wYg8zTDFJlhDtRaycOoKSikqlVkG+1toPYJAoQLLsDfGrU3e5goChUlLymW352CtoXvjHtW5NsQZmReQY7hy9SzPPHaK1VffRArouD4PDw4oMsN01WGp7jKdd3n32g5bc0s0HEVysA9zikoOznCyH7mFVFuUcqnVFE3jUClMCaeX6gOUpjgEgFtradUrVP2SziKSAo3AdUq0p1LlplzxaGsIMPboTPz+xjaziyvMHZ9j/uMvoLOY+MEGWTxCeC7LlRq1G7cpol3qTZ/5pXme+qs/zc3BLnZ1lceas9zu75LHCX/8ha9Qm59j4+46vYMDNjc36LTr1NstJP8Brhgenw7R+ZiWzPnp58+UF9vOImVBlDzg6hOz+BxnZmqZe5tdmirhuUvznD55inMnHiOOYgaDAYXIePP62zx/6SSakvQq1zmN+kWcIuXV19/nH0zw42/8wiJK1ymosdybZj/KuF/A1/sZ+7EhcCTSHPCfba9TBA1u3brOFZ2jhMTHECQjphxFvCN4tRC8FsEb3QinWmF+tsNpkfOx5SnUtcmwascp9+TDMERbcBBQGLQsUV1Z5pBl/UO1ovLhmq03mPfbiDRC55axzhCmbLUYYzAm/6ANEQQ+jnRLtOsEy3JD0Vsj7r3NY/OrRKZKOipohB3SsEEmNJkUJFSJTJMsMZwNmiQ7t3ji4mWKn/1PcNsdvHyV4nP/HGs26KZrvLvtcnu7SSpD3EqNNJzMW30wPMCRDnNzM/T7XWqVEh7f7/fxlSLuHeBZRTHOGOweQBxTkctY16cwOYvH5omjnOFwTKJTakHAVKvJMBkhfY80La9DHE3eAwZFkhW4gYep1qlnHkZH/LEYMrA5fxswrmS3u4roeVy5fI6/94/+EQf9EY5fBemytDhPHh2QJDngMBrHqDRj+/4tZhs5626PPJwcLE4e63A/7pN1D2guu+xvr7IyXye0Z9jd2eHmD7+EGywglIt0LZmxpHlaUj5XfRCC3Sgn2d5kutGgHQZMn1wkcCCLuoxMBVEUVNRkbh2bpBgpYV/y0oULtC6cR4c11t6+hd/dYroaYLbH7KaGwcEA49f49U88zhffuskozUitoFV3Of/YGRKpWOqAtgP0eEDDz6m4PtJVzB6xvdRNoJ/FCOUijMBT3v/P3psHWZbl9X2fc87d79tzz8raumvp7urp6Vma2RgGzcI4QAMSgQODFtsgwnaE/nCEvBGysf+xDYqwRUgWNsJGSIRkdg0mApCA2WDoYaZnet+qq7qrsqpyf/t7dz/n+I/7qqYZTWcOdkQDEfWNqCUrXnaf9/Lc3z33t3y+eFLX+N3FXIkQ8k+xT4QUjMdjpFK4vo+ochzlLAYPLdpY9F1Oh7ELrsrxT2rixi2eG08J3/cESw8+zJPPfplmI6J9agV5YYubd17n1qbl+z/5H7N55iJXRimf/rVfYVjO6DkBoe9z1Gzw9NWXUY5DdGub1ZUWqytNLl+6TLe3jiTj/e87edAH3uYgngkHbQ3aupSZQ1mUFJUlLTSD0Zw0S2hHCeN8SNzzeWxjk6JIKNURV29OMboOVlqUbJ7u1BwMK2vWBlBWGUVmmObHb4ZuK8enjxR9zIWI5sDBnZdcjF3uzAxfHpWMK8vnfu+zfPKvtrj64stcxWUJSdMxONaipUBXhjcSn/eElvZyyFw5ODJjWWsOjvqkJ+SAq6JEa11vqjKnqDSulPVEppFUlSHPi3tDT0JAFIY1NMsN6pugK5FCUFXVIrenkEIilcQRLsJKxAkgLmUPQT7D2toerUaMNjDK7hC1HJrLLo32Cp3l01x6+BztdZjm10n7E5QOEQfbYF2s+wQ0LuApEDZjsykIgghZVdx031Nz0PXx/bd3G8M0liBqUC4qf75yCWxEOpxQFgXWqZ9Wirx+v54XUszrVtQ8T5HKEDYCpFDkuSYKO6RVRhQqtBbk+fEXqQpDSmvIEPzg3/4PWdIBz33pa3z+2pPMpjNECbum4FZRcbnhEXmK0PWJN3oIJybThsFogkOJkAG6mKGLCVU6IsuGNEPJQ2dXOTw8PjcvooBr29sUacJw9CJBJHnw4hbf/h3vIkk1Tz/9h5h5QqvVpUh3KKqc0SDHYFGBoSgEa1unWV9f56WrV1FCs9xboduJCQNBO44o5ppmcPx4t64cSl3hFIZZltMqNTaybF7Y5OYzL9I7tV5jCCYT/MClyDShoziaJAhhCV1FHHfA8ZkVFZOkotmCc4FFeRXSsSgpCIvj92nt2CXroSlrqHTNUjIY0OZPeRPcVVVYBpOMIkvJjMTexbta7nGZ+Dr76sTOFIBut0UVeby2t0PSbuGFPlYIoiCi1JYiK2lYl8//ym8SuRG3d/Z4tZpiAocwy+gpi9WawPOII4/Tp1bp9Vr1+6pcgiCgrHIGw8MT1wJvcxD/3FPP1AB9YZCurgOTEkhXYZUgXpUYCgo/xJeCQTakKBIifALlIpz6B1DkJdIGdU95ZVBa4CoH37F0T22h7fFvS+cwScdEUcXphz2SoUAMHModh42oxJM+z00NN669wr/+lQn9oyO255YtYbgQwEO+4qyjkKXlHY4hcmFkIZYVTp5yoRfz9DzDaRyfA87mCRZIqjmtOKbUFb4fEEQN5oWGStb0PXsXWSDwF8U6oQTGgBIuyhUop36dQCGlqgmRlUBYuYCGHLOOyQ3OtBNW/JROGND0FFYULLWP8Jd8Ll9qsXbuCo1Ok2yyy/y1ES0U6vIKyfAFlL7D5OpTRNajoxWO1egyoyFCluOIZxMHqQQntQ0l0xluEFBUJUo55EWJ7/ukaYKpDFI4uHW2iCiOiJst4kZEpjV5XlIUBVEjoN3q4AYOVZaTZ/Xk23jYJwgWQV8e350SNttUzQ7dtQ2+51N/jVXrstJb48Xf2CHffp1yWDB0LJ0L66yuLrHcbJDMUnorq+DG4FhGkxFZOqHXXqHIErLpAVXSR6mK2HHoBILG6vHzDFLO6LUhdzxKqzEZvPTSbfb3R6wstWu4E1NWNzY52B9jTLIoyhryWUqrERGojND1yJstVJWSpDmzLKPRULjjklZng8n0eFOISksqwIiCo8GA1ckEGXs4cUCz2eXlf/sKkahvBKeMTyOfc2s042CS4rouoLh254iLly9RpBmTBO70D1h+wKfZ7dYOX9YQFMcferSuUyfSWkpj0bq+Ju4G3m9GQS0rwXieU1aayop76I+7pENxF7KCxVi7YPIdf72c/ei3Y4WgSFOuv3GV+TwlzysObu9RFjl7+7fQoynz6QztSjJhceIYvzIMjWY0GYHJ2FzpcPHyaZrtJrdv9zk6HOF6LmceOEUQeGTp8QXnu3pbg3ijURAHPr7v4AT147+0dREir0ryKgGjyGZQTKY4jsEPFEVVYZ2a8SGlpCgFpa7foNB1f7OyMB3PudI6zXR8AkhHOKhIUXkaT2Y0liy2aVGrPqJy8V5TiDkkS+9kMJ6T7t3iMLNcs/CFzLKsDOdczYqSnIkMtxN4dlJSSEnbldim4lQrIg+PP3larQGBKxWZljhujHRj8gryrKAsXYqsWiA0BVLKOkWiFEh30d3g1sUcB7BQFBXCCoQBF4GuKk7alNLM2eotsxoYWg1BFLj4nqbVyvHcgiD7EuXeq9zYVRyN5rStRyR90mRM2O0RdZsc3TzAHexRJhY3jHCCBrZqkBsP6Vk8x0GcsN2qssTxfVzPJc9KEBKLIEkzBv0RkefjuxLfVUhX0O20SLMpwg1YWVlhNBIoxxBFAVlZEPghDb+JFzcYTiZIm2ONJEuO/7kIVxF7AR/+0IfY8Bs4Sc6VSw/yUKfH7M51Zq7l0fdeQV5aZv3MFm4QM54esLx+CkOFMRZHynq2oNKUZUWel+RpRhi2aHc6JKODBff6rTWcTClNie97TOY5aZphrWUwHHF9e4/N1TZnH2yzcSoinYe4ruKOO0YiEUKxueKzvq5wdZ8PvfM8lTZc2z7isJ9TjAvCWLJ86hT94fGtn5XR5KYi8hxG/THJ0SHtdoDxYpbPbvLkf/2LRM2IQIb48xk/8pEr/NH2LqVR2LxC+ZLD6ZQbO4e0I4fpeEY7CsA6qDDE5FMoQVXH71NtLIbareiufUOd/hD3mOHfqLwyTJOSylgMdeAXog7b8t51YRencYux5hvB4/+OjCNJx1PmsxnSd2kHIUWhSWYJo/mcozRDS4ONXYzRWFsHfCdsUOqCTltw+cElHjh/hqB5DqkUyyunKHJDVRUI6YKGYnY8LuOu3l6j5JaH7wh8x6EqLdYKXAXYilDAsKwrEqFjcDBIPKgcTGXRuUWLmnFdAVlZmxlbAShBZcE2XV7afglxwrBPkpVEYU13yzKLFBlFZeqhFCsILwWcnyhSlXBmpcHp+Dy3nn6F/UwwLSqOSsNBWduxBYmkWpwMhDUMBOQknA8LqhNynlbXLJkoDJmUktD3meUWISxlaUjSnOKudRwCJSWu59ac8sXjnzYa1F1esqWqLELWWANHWIypakf0YySkA6HAaYYsNQydSBMHPkoorCmRZkw5nyOsoAEoN2CSCcQ4weYx5QzcfkTWz/FcC1GE9mL2Dz3Gqo1jc4QWJ3Le41andlkyIGRFUZRIpdBaMpuXSK1wlc+8LLBS4cQe49EUSk2nGVBpzWQ6pigsYaND3Omy1G5TVYbQVVRSUWZ12uU4OUGA47pcuXQJkRUYU9ECHp9Y8olAd2K6Z9YYiQJBhfIklcgoiilGWIoS0BVWW0pdgFQEUYt83ieOu3TbK+wHkiQ5Pp1iKnA8n27YIskHNaVRm3qi2Qru3B6S5wWj/gRHSlxPUUmHPC1RCO7cSRBOg16zz8FNzV6imGYKP2qTzGY0l9ZQcRP/hIKzFJaGHwCWySTBzjPseErpFgS9JVTHQ5aSsc2JNz3+9g99F7/6E/+EQZ7T8iTrscfIcXnq+Zf5zvc+hnBcTi979JwpqsjQ1lAYS6ZPSKdQT2AKUbt3WVubQ9TGDiC+iQNyaWBeWrQVC//dhZ+P/UZu+JtO4ie0p1SFwQ9i/EYLKyVFXtYH0bKk2esSxj7pbEo6GpKbkiTPMHlO5GdcubTOA2dPsb5+CunE4NafqyvkAglRfwZSerxx7erxC1no7T2J55bAgKMNStamyVZrhKhdXzphiLHguR7KUZS6YJzkCKVALPLCCDQV2tRekwoFCLKywCoHTIE9YTMkiWWWFFinxLM1PCjNShzPqacXTYbQGlE8Q0NKGh3Nue8QOEWIngnGc8XBuOL6YM5hanCQmKpOrGljOJxljCZQnDA00ApX0cbgKEVTOovSuAQUjvRI9ZQi1yhjsNaByhI1FGlV1k8xUiMsKF33Uispa49QY7GVpjQ1bCzwju8TN6nHH38hoeW5fPSDgvWGTzOMySYp2SwlsiGByZA1rxHpaFTkI0VIpnOMKnCsJfSXSbXijVua21nModpkJHuUegLKoREevw7l+7iuqv0Lk5TID1jqtml4PpcvnGY6GVKVFj9qIKSgf9hHawna0G63aLVCtF7FGIFrclw7YzTOSYsCL3RIixIn8lhyj29P8XwPoRx8aXE8ycSD0Coe21jjha9VXOtEyCohxrC7ewuVZiyfW2Zn9wZKNMjyCtf1WFtapRKWVmeLyDnFuNdmNJrVE8OuIjphkvaMOsUj74twleLR7/r3ee3qTVTZZ3g043f/7efZ3T9gf29OmVd88H3v5EMfeoxu5yVuvrFHL47YWFomnfXZfuMGX3zxDstnH6DdabE/OMT1A848+ChFNsacgCH4nd+4QW/ZZ2klYM6U9ItTPjJ/nJUHzqHliOihJcaHc574vif47scvQhRxs5/SDSPO9zr084zxfErsu8j9V3niVJueHjDbL2lFDllRkGrByDk+/VgUNRANsQjcdkE9FXdno//dU0IK6GlGWi5w1ovXiHuJ8Pr76saAb0Bhv4U21laZzWaEYYAKWkgpyPOCssiYRg7dEA7297G9gOWOoN3xOHfhCZRsYozEkuNIUKIusFa6wJgSx6lxI9oK/uRzX+arX3qKv/8//oMT1/M2m0I4YARS1Y+aBr2wU6oLcS6yri5XtYkxUuArrx5dtTVcSkkJRiBkXbwsrUBTFzitqQc17Ak84LmuPR9lZcnNvL6tC5fS1ibO01QTubV3pnIsRSGxxpJ4FZ0Nh41S0Ms8GhPJ1Z2MPDMMZhWlqUtzD158iN3XtxHz4098RigMgqICg4vWizoBFdZq5vN5XcgD6o0m0brAmBrki6jz37aSGGMoywopHByn3hzaaCptECdAOh7YOksxz5iNdnjqhdusdDTnTzfYWFql2ajIBhNK7RK4Lq50SAChDJ4s0NJjkFdkRUU5rZiwylxHzFSDVAYYCX7QJEtSxtXxn0dNs1SEkU829ZHSoyjK+r3oHBxIZiVllREGHq5ShL6PtZayyOs9IQxSKebTMXkWUOIzns+QjgDPwQhLp906dhWB5zGdJ+zs3OZdly6BLOmT8qQ75sUzAefW1hCtFltbHSgrpk6EtS6hFyFwiIIAIQShH1Aqi+u5SAFB0EU5GYPBDp6K8U/oCukf9knUGF2VvEfBX/3uT7B/6ypfefJ53v3ud/P8s39CSMZSu4EsMl577lVEDg9srnJmucXR4ZjXX79JlU9JREwhQrTTwwkKinxMXsxJkjFFevxj++0bCbduZIThjE7X5aA5I3KbfLLXRboVW49s8OhGzPLli6gg5pmnryEKw4WLSxihOLi2D7LGES9JTcdNkLYiyS1+EKBciSMF6oQAqu3XD8l1inHxOLoodr5Zd4NxqRfg6wVl1L7p+xd/q/9Y4BmstSemU0bDEVtbWzQaDUZJhi410lSk0wH9nVtIkfDQxS7nzoaEYQeh1hBqHWtzwFDkAVVVoHVBWWVgNaqeUkIbzd6NQ1545nny9KQuqlpvaxBPZO1j51SayPMRQuApB6sNla0r1FIIrDFUVYnQELgBd32p71q7efhETn2qK7FU1hAqn7ysyE1JwfH9pklRtyEpZJ27tAJbaShlnTMtDGUFgVvf6bPCEDiKqpTMRJ3ztMLDbXqcPg+mFHRnJcNpgUDRanocxT7lCaYQSWYoy5Ig8BcccoVzl/mgC7TWVJXGGgML124pJUJKlKwDd6U1rlB1YLe2NqhYvC4vSlzHqRnMx8kU4KT0NpZohufQuuJmP2NvPGep4VIONK4oWFvt0PRCqAr6s4KKkLSA0nogfeZpxUA28YMeFoU2cwJPM8tchBPgnmS/pWq+ickqonaHwWCC8h2s8ElHcyoref2NAx576J34vmSez8FVxGHELJlRlQlB6GI0FMTMJ4YszWn12miTIFGk+ZzRCbnoyAuwAl555WU+/oEPIIRm+403GEUOZz74fkSa0zt1mpXVmHQ4QUZttHZx3ZDQC0FqlHRQjsc8GxP6LlY4GOuTZhl5OiSbVUT+8RN5e+OUfDqjKnMeff6r6GzMbDxhOuqz0o54/NJpSA4wpSEfDhmUYD0fU5VMxhWv3brN7mjGcivED3y2b2+z3+/X9m1KcufGa0g0RXp8DUkoAVaT54b9ATw1LAiKV1nutnjvYxdZP71Bc7VLVeQMbuzw4m9+mUJZwpaLcDVGaISxlKbixjDB+gVnT62QCkPT73Dq8iO4jS42OP7mau4G2G/w4BWCb5oRt7Z2UzULBPY3nrLf/LXnefgqWPz78fvjzJmztNttlFI0NczyCcVsQDUb0nId1s9s8uDFB4jDZcoqQAsPbep6n9Y1M7/OlSuko7C2hv1ZXef7X336RYosw5yUf1xIfCuPD/d1X/d1X/f1F1MnoKLu677u677u6y+y7gfx+7qv+7qvv8S6H8Tv677u677+Eut+EL+v+7qv+/pLrLe1O+VnfvVLVoh66EPKr98/xJuqzTXE5k9Xn+FPV5K/2Xjt3e+7+7q/89ff85Yt+wd7A6tUPa9V91vXbY9gEAvTZrXopBBCgLEIFtNXwqBNhZDyXpVcV4Z0XjCbJszn2T3mgeu6fOQ73/+W6/iff+zH7NWb20zynFZniU+8+wmaccxzV1/hhddeQUUBnlD0Z2OsqBupVh2fThxyOJtx++iIIGzQ6/Rox208rx7jnk6nuI7D6uoq1+/cQijJL/zmr77lOv6bz3zNOnFMJWqsLtYi7V0XFEM+3KPtO/h+gPGaZG6TiZCIRcuUFuKepZVjNR4W1wB5waw/QJoMI10cx+UffM+H3nIdf++//VtWqQW8KwyQUtzjxkihGOxP6PcHtFotsjRnd+eQ/b0h3W4Xx1GUVUbc8MA6uJ5DGEtWN3vEjeCe04uxBl0Zfuq//8W3XMe//MX/y/pugF2QIy31b8IasFXN/NElVmosJcaCLkqMU6/XFwolPBzXR3ghUtQ0xTd7hhlriR2P7/2BH3zLdfR6Pet6Aj+QlFndjSRkPemsdcXq+jpVUVCmI9Y3lpBSsLR8hq888xxlVXFqfYkPPH6Z93/bt/Ff/f1/iHR8kALpuORVwrs+cJoszZnPc5754vW3XMff+ZG/a0sjCDtbzKZDXJlhKWt7Pi8map0mGe2ibYEtEpQTUOASt3rkyRxPWrSBG9tv8L6NGa+8+Dovb99m6i3zyR/8z4l8jxIXx1T8o//hP3rLdewNta0HKmvCpUKghCaZH/LKi0/jBUN+53c/zcb6Ohvdc6yuNHjj5sv0OmcotEWbOePJEa1mB087tFoXWVnf4NlXP8/Va8+wvPRuvudTP8DLrz7H933vW/9cfuhHv8P2umuMRiNwm1y4/ASt9hmeeeYL4Hp8/4/+JwuT7zd908JD/e5kKAtf9bve6toaHNfnmc9+gYNXbrJx9gKDw0P+t5/88ZPIuG+zUfI3Cb5v1psD+Ju//lY6aL7V1wEotx6KwVqKskIbi5BOHcylSxjWJsTW1heONvXUZBBFlGWGI+qBGnn3/7kwLlBK4roOzWanvtCL48eZg8yQTKZY36MoC24e7CGV5NrOLW4NDhGJi6ccKmxNYTNQGEEuBA9unmatu8rN3V0wlmazSZKmzGezGpDlOCRJsmi/OkFv4knYu18vWnADNOHsAHE4QrgOTtTFb6wQ9M5SSYGUCs9ahK6QusSZDNCzMdV0gknmxAI65x4htQ72BAas44LvO7iuSxQ1F4NLhqqse8W7vSbWlkRxjOc5ZFnBaDjHcRxcTyGVi1LQajVZ31il0XSIYgfXVbiei+t6WAHFCZY6UauF5wYYIxA4GKsxpsKYAqMFhUkZZ2PSagKmoN3skOsZSV6AVXSjNs0oQjkuXhwihQuiHoUXKIQq0MYQnmAaIqSh0QxptWMO+zNc6VKWJbY0REEIpiSZDXj3uy+zvLzEH/3hV5jPJGVeEDcDVpabbJ0+zY2buwjpYhFokyNMTqPtI9yUQAnc4HjnpyzPcTwfV4FUAhW0QKcYU2F1hU362DKph+UkWKPJZiMCZbBlbZhi3YBLly/y3vU+k50Dbu65pKbC9G9StnrYeBnnhI2qbb1/rLD4qkQYwXw6ZG/vDcIAXrv2ErqckyZjRDth3B9zuHuTpt9k73CHRtPB6jnD/oDYW6HTK5jM9rBmji5GKI54+qnfIc2Ob7kcjSY4jkfcskxGJY0owlGata1NHnz4XYiywvvGvf7mAI64N4VtRYVdYIUca9HzjAuPv5PuqbNkX3vu+A9kobc1iH+zEH7cqfqt9I3B+rjXftN1SIuxVe0a4wikdOq+XlV782VFSZbkJPOUnd1dbty8wdPPPssT730P3/up7wEMDha1sEwzssJ1HTy/vlizrMCY4i3e8dc1Smcsnd7kKJ3T8mNCz6tHdI0hiiImZUpZlPiBjycdYtdFaciTlLQ/ZnP9FFEQ89KN12ukZRThOA5lURCGIdYYHMdllh5Pqbvbf3+XBompXU4cJfCF4FSvhTeZIdMxwzu3uDVMyZYu4sQxSjq4iylc11Tsv/wc5XxMJw64dOEcD168wOvtLntJSXZC8JQL5ospDbbSOI6LdCXF4mdlQwc/cBEYwtBnebnLre09PF8RBC5CCtbWl2i1Wjxw4SydTkwQutQj1aZ2+BGC8gSqoyxBLm5iAgO2oCpT0mJGViaMxnvMZiOybEIyHZA1unRbLfq7+8RRB5ZdLD5G5SR5RhDGCOnhqAApfYRmMadwvBqNkLW1FYyxuG6KlJJut0MyT8jTgmw25sypNR55+GEqXXHu3Flmk5JWM8ILHLD1NPTTTz+Dcl2yMmV5Peb0uVX8WKGCAmPgcP940xClJEoIqiJFCvDiLvmsophP8VyXyXhIqStaK8u4rqQ0Al8Z5sM9lKMwbgONQeea4XCGiHpU/hHL7U2cckY+tYRRi1F/94RPxCKEofZ+mDHsD9i+cQ3PK6iKKft7N3CcCkdpZpNDljo+6ytN5tMjXAHoElfAweCIPBa4RzfRpiTNBgRuRRTMmIyukZ8A4mq3IubJiLIMSJIJLzz3WSodcuGRd7B5aosymdLqLd+bHDf27pSSqLEJC3qiWcx/WFHnAGwJJs/ZPLvJzsEBvn/SDqn19p7EqZnYbxVzv1ka5bhz5J81eN+V8uoLHmsRVuK5IbNZyuRwxLzI+Kf/58/x9JNf4WD3AOE6vPPd7+KTn/pufu23fot//L//DL/4z36eZhjjKodG3ED4qqYxypg8LxEoplOH9ISJqx1dceXiQzyKYHo0gCLF0SXf+cR7WD+1yS//+q8zz0sSU5CXCcI4rMddjLQUxYD5nYyHLzxK+x3v5sbBNr4fkM8zKg2edFhe7tCfD5nMjr9IHVWD8o20GFUPQonFSHNpDYnQqHJCp+izZKecUhm3rn2RLC9BOfXUaVlgjeGR5Q5ux8MLPSI5YXp0jVlzHRpNAvf4CUVHunhOTWmMw5iizEnylKJIcFyJ6ztsnOqQpSXWSjyvPiXP51NW109zGlBzCgAAIABJREFU6fJplJvhOi5ap4xGJdlBUk/5itpAwpr6QjpOpZKgBNZWzIuENBmQJENGh0fYeUZqhhxWA0q3wPU0IS7Nqs1ap8XW1hmy0mVYjRgNJyjZBGFZXV2nEfXwZP3Upq2klMeXpCpd8vr121gdIUVGt9tl1p+SlyVSWv7Kh99Pt9MjdJcxbs7prQ1WlpcYTWdMpwmdVoNnnn2ep59/FkM9WJJkFUmeM5gmOG6EtjnN9vGc90qXTGcTvCSjvbKO21rBVZb+jRfJkzmt0Ec4LsODHF0VhK0VhC3JkwFGSIoiw6lKsjTlC0cRw7JLsHKB/Z09Xr19hwdPbVHs71DMjz8B/+7/84946OEH6A/2uXH9ecaDMZ1WRFkMCX2P2fwmjdgn9CuyYsI0d5jlI2aTCa3WOV67do3+YI9ed5lJPqA//Cq6TPC8nNAPeO36czTbS5gTPFinRxN6G2067Ye4nT+Hqzp4jstg5xa/9E9/ih/6W/8ZoawHesSCWY7jUiRzyrxACIXRun4aC7x6ryGYT+fcfu06ftTk4HBEdQJb567e9iD+9STjt6a7BL9vlir5/zqoZIVeJH/BUT43b9zh2rWb+K7LU88/zR9/6Ssc3NimGcc0Oy16K0tI3+cjH/0Yv/5L/zf/5Gf+D9712GP0Ol0++rGPIaVAKYlUAtd18AON57eRsnvsOr74/Nf40gvPEHo+K60OZ1bXaDUaTG/dYXtnj1maM7cVypEE1sG1YpHyMVhhwZQ1Cc9q9kZ9hBE4wqXT7LC8tMTmqU0KpWg2TwAcycUpXN5jc9acFGvxdAHjPsPbdyiSAaExGOOQakNWlXgY4iii02iRpAmTyRgv93CShNlkit7Zp+o8hNOSiBNQtFEUE8cxQRhQFAVSSMKgpvNZNEHkUeSaMPZqtoyqaDXaOI7CasGonyKVZHW1heu4JPMJstT3wEietZRApY/nzQtbn8S11cxGA4aDHeazPtPJERudZYaHJde3b+G2XdZaLSZJxkZbYsqKYX/Il59+ndb6Cn/tr38/P/k//TQbGz3WlzfwlIswFcLIBYfm+P0bN5sMDsdYkyOlIckzKl1RFjmnlpfodrtgJfPZiKoqcfBQwkFZS7cRs9Tt4joenes3AIEb+RivIogsgR9za3tC1HOQ7gk3kyzBVgW5FkhdYgbbaJ3jRSEvv/wij1y+hOcGWFMTMyfjKaFvUW6IMJYoalFOxziyYpgJplmOK1zajYCb169ybqmJj8DMx8eu49mv/Q5vXPcpqzkbG+toPcb3ocj7lFVAHCnCyGU0PmRr42GEp5mkc6xsMM3HjNMhftNBBtBo97hx/XXOnlrGUYrJJKe3tEGpBf3h8TeT9a0epdX0+3tgPebJEb1Ol6PDGWWZceeZ5wmae2gt2e81kNmc1YM5yUoH1nsoXHRZ4QQu6WiGH4d4foApMrTO2d2+xZN/9Hl0kQE/eexa4M8jiP8ZZS21GYT8+uP+/98pU0uNQhNC8Mqrr/Grv/KbHB2Oec/jj3P12nU+/sl/j1/7+X9OksyYF3P2D3fZPzqkEYZ8+4e/g9/77d/GkZJv//ZvB1ljMJWShGGA8aDZiu+56xy7DkdRCZiagvHRLneGfeIgRInaTduPQ8ZFRmQUbeUQCoWwBkdYHE8RChdb5Vhj2Rv1cXFY762xsrJCr90jTQvisEGZHf94qGSNubVSIEWdm3OtxjMVzTJhPUuQQUA+k8zGM5JSEERNlM1xHUHogesKXCck0LWjTJLlzOcJjh8gdm7gG4l/Qg747JnzeJ5HVVWMxn2U5+K6Lq7rMJlOGIwGaA1SuEgKikxjc40nPPxKoqc5R7MZ2Sxh41QTXWQ0vBBrLEYbrICsKqlOGKtm4e5ijKHKp0hbYKuUYf82rQC6jWW2ulvMmeAjcVyLcXLcKOLFl2/wxSefprm0ygPnH+fW9iHJPOWB8/u4DzQIwwhpv0XbLbcu0FZFnUfNshRrDYGnaDYChLQMBn1WV9bwvJAoDDjqH6B1xWw2I44jgsDjwQfP8q53vYvpfMrzrzyL70t0KcAeLeoLx5888yzF9Vw8P2DU34H+HdpLPWyZkCVT9g4P2TzdIvBclPRw8RBmjlKCylTkyYA0GSGZMytjIGI8PGKeTKnylOn4Nvl4nyQ//sl1Nr7F4cGYRtNjY73F2lobz7P4nqXSGZ7vMJ/PMNahPxohVEVpagtElymNbkhRzJCeYX1tkzs3tymKnK1z6/R6LkeTMY7nUdnjOd5CQp7m7O5cRdoeYbJDOh5wNJxw+cFLsD8hffEaUavBZKlFmSfEM8Mo6dLywHghxioCzyUrKwIrENpQJClZnuEXBbqsyP4islNOKmx+M0VxCyzk+Rxjav72Xb0p1fRnklLqXl7qX/ziv+LGG3fotFd548YNNjY2sFLS6XYY9vdJi5zZbMqtW7fQRcF8MCDPM964uc0P/83zNVjJCKRQtTmrI7CYxdPD8esQsHAmsaAUc6uZzScoIfCUi1dlCEey1l7l/MoabpLhFHMi38UVHi0V4UcR+WhEZS1x4NNsN3FcxTSZ4oYxVaVPhMsrBVJJtJBIBIHUBLogMgWNYooY3MZMBzjWEoRNjCdZ29ogkCWz2YjJdEaWzCkrzdnzl5jMEqqiQvkBnueTDe7gNZqEvaVj11GlOSYvsdYQBkHNdgYQEs/zGI8nTCdzytLgugFCKEqTM5/l6KZPd61HK4zoD/rsXp0R+C5Os+48Ko1mnCdYcXIazlqLNRZdVcyzEUk2ojIJYdNnb9Dn4XMP8Z7VFfbGb5Ckh7Q7MeN5BpnLi69sM00q+tM7/PRP/2PSJCPPUj732Sfx/ZBLlx6s//vfwp6ViwOCMRVS1myNIHA4f2aL1V6HJJmR53OKIkeI4t7hRqra8i9NU7IiY3W5S5El+EpxdnWLMA6otODla68DhhNKBBjq7onKlDX1sMpRdoaYD+j4goOdHfLCsr6+iiNrH9giOSLPcqwxaFsgrMH1LBqB4yjyrGQ2zRAC0mSIlQ55fnwjQBjHWFyKIqe/P2VzY5l0XjFPFMKvOBqmHOz3WVpaoj/cocgrHr3yGJNpSqu3gqkKjvZ2ERrmgyHntjaBFCFBkrLSOUeiK6R3cOw6yiQDCytLSzhug/lYM9gb0Ok06bS2ePrwgKutsN6z27cw1vJG3CKajDkzGBG4U5aW1shnc0pTYsuSoihRAgLP5cb1V+m0G/TLv4CmEPc6rBY4SWEtVmjE4sFSWIkQGmyd7LcCPv9v/hXNVszlKx9AOhFiYRysYOFYIxfwp299HY5UOFYhrOTqrdtcfOgSUjpMpxPkkebJLz3JweEBoesh84pXnn4RW9Wn1DNbp/nh/+BvcP7BC+zsHLC6uspSu4WhpCbqLjoQxMl5o7t2ZH+Kc7PIk2pTMU8yTq2v86lPfR+PnHuQa099hdHVZ4gdhVeB6wVo36Gfzllp9Qg8H7CMZ2NyP6MZODiB4mBydPw6Fi2WEoXQFmUsIQa/TKkm++zvXsM1GiU8Kny08og8F1eneI0AKTQ7+4eURYkSlmYcIVAcHvWZjacI11BOe5hi69h1HOwe0IhjOp02KJgmU+bzOWmWIaUgDAJ0ZclzjbsAqLXWG5hKUzUhdUu6TpNBodk/7ONJiV6tiFoxxhXsD8a1IfQJIC4AYw1FUTBKhgzH+6TJECEljt/kiQ9+mN7SEkfTI17ffoFnX3qKy2cu87uf/gy7+xOMUCBy+v1dpJQ4StE/GlGWFVADzaw8OYoHoU8jjshkQZ7nhIHHow9doNNucP78efIsod1uMx7PMNrSarcQAgI/JAxCjNY8euUdJMmco34fRyjWl9YJGzFOELG0/DTLK8v39txbqdPrEcY+Qnoo7eIIQzoZIkzOuVPrPHf1Dod3bjEdDWg0Gyyv9HjowS2KvMBRNbBNAMIVPPeaRlcVaZZgjcAPmpw+e4HIcSiPz3IxT+YIESKFRRcV2WyCHzpMJ33iTsSwn+O7yzSiNR546BG2Ns7x3vd+EINE+j6OkBRJxrg/5Pf/zS+hZY5QkuFwBmVJb6XJXn+HKIiPXUeVFUS9ECNcbFWyvnqBYWcF5Xgc9Of8/h88SaezitfsUfmGpmvwwwa5gcvW4/s//G7Wel22dw4pdMF0aFBS4gce7UaDz3zm97EWWq3j06B39edQ2Kxl68ZbhLUgShzhk8xndHpraCqkgd/69Z/l1Zc/gzAOzc4SZ86/E4wCKnZ2b/Avf+YnENLlx/6L/4Vma+lE+thdSavASspc850f/giPXrlCs9niDz/7B4yODnntlVdYXlriyqNX+MAHPsCjjz7KAw9eoKqqe7jYamEV9fJLV0mTjPPnt7h4cQsJNRVxEZiPO/R9YyH3HqUNkEJw6eEr/Ph/9xM8+ujjHOzscPriA9z8Yped555DFRUWl4PJgKPpmHmSkKdZbQ4g657onobClDje8UHLt5JYA9KSuyWurZBpxXT/kOrGNlc2V7BFjisFrnQQwmH75jWSXCMcQRB6LPXWKLKSp7/0ZTqdJltb65y/sglU3DRdiu4mbnD8pkwTy3TeZ/tgl/ZyfM+pXrkKIQWtaInuko+RCs8PEGhOnV6u+6ctiArKozmDwYAnX3iBtXaT1KuQpU8OVGnBcrtD1DielmetocoSDu5sc3h4i1neZ55USBHTCgKySvHSq9e5+trr7O3u8zf/xt/j2973XjyxwZe+8uPMZhPiMOCJJ55gdfUUf/TkH3HpwioXTi1Rzed4UW1ZdhIyeTQ4ROvaJCP0PS5f3KLXiVlf36AqDd3uKgjDUneT4WjCdDJiqbdCms0Zj/s0Gg1uvPE6RV7SbnfpH/W5eOEi6+sbFBiKJONw9wB9gn3fz/2zn6cyBUIqXAxPfu4P+Ll/+FMc7O5TuSFr7ZjIV9w+mrOXFrRWlvhff/YXUELea3GVNYaUD/+VHyTJBuTFBKUs7/m2x/jZX/jnfJ3T+dYaTwa4Tsjm5iYemjOnYtLiALNWMZ70+dEf/k852JvRPxoy2J5xcO1FvvaHr+L7IWGnhed5bN+8SeD5zKdzVlc3+MqXnySMXB66dJE7NwY4XsRk7/j0Y1pIDq4PEVJz6fRZRrdukiSC5dVzGOmii4peFPP+1XUOdE4oc7y4xQtGM+kP2d+5g2cMaZIwODhgPBpx/fob3Nm7xXzUrx2whKbfP/7wdVd/bjlxZQ1GSCo8lHApyozXX/0qkV/SbKxwffsN/uSLv0W7G9PpLXNq6xGMEWALjIUbLz9LoUt0WTEcHNFqL33Lp/E8y5HCIZ2nbK2t4SKYDYf0Oh3y+YyPf/zjfOxjH+U9j7+TdquNxZJlWT0EpNTil13kWitGheZrz7zCgw+coW4/XxDArf162943kfyGE5D4Blxmp9VmcjiiSgvSWcLnP/8Z3vvweVTU4KU//gqD/QFnH3mc93a3uPnbv4EXhAgEcdygGTfwPRdtKsL4+D5gR9ancSMkpazTGAbB0czSvzNElDt4ytBphLSaEc2Wywe/69vIq8XNTAt0ZcnmGVcevUIcBUSBgy4zDg73GUwlqh2iTvC2DKIAKyyVKWjpGkVclWXdJy4kxq2wbkUQNnCtQSiFozqUpkRXFSbLELOCWLgoIWi2GiyvdCBwmRWGg/GQWVYhZic8pgqDsRohLdk8QVSw1t7E0mJne4TneHz+M5/lX3/6N9na2uLjH/0Io8EZ8nRK4ClEI+LKQ5f4jg+9nzBssbK2xJmtNXzfp9AGrfXC/OOEwmYYM6sSlFLEgWRpucXpM2dohG1m8ym3bt1ECEmntcb29i7KMZw5ewpnBlHssLKyxng0ZjqdMpkOiRohaZ5w841r5LqqT8m6dtM6TtpUYCXaWPauv8zv/uq/YLx/g8hxuD3NaYeKUEoajmCWW3a2d7h69TUeufRQXdNCYxFIrUnzHE+5OFIhZEWn18FSYaxEiuPD+PLSBlVpmYwzlldX2d07IskOmMyHhGGbr371TzCVz/VrryNNxCMPvYPdnSOEKGmpgP3d24yG+/Vch/aZhxWXLj5Oms4J/RUaUYQWmnG8fOw6Dm5NiNohK6shs6mi2zlFt6WYlCmZ0XhBSLTapb8ZcOfOgGbocHq9y0ZS0O60SA7HPD94ndW1h7l9Z4fta9eJW00eW1rjmcmAsqwwlDVj/FvQn1sQN6I+MXYdC2bMbv8GX/i9X+Zgb5swiEnLhEarBdpy+vw7ccMYU1UY4eK7AXde/ixbDcHOuCRLZn+mdkNHOTjKY2rmDA4OGB4eopQizzMuXbrEJz7xCQLfQylBURUopYii6B6TWEpJWRQY6hahza3TXH3tddK0wI39e33uWmsc5/iNeffGcDegV4teamMMnnRpxk1UM8C4Ap1bPvcnL+AFkrHT5I35EXKS0llfRbqK0hiMcEAGFJVkt7/HvCwYT45vMbzbJy5E3WXjWPBchyAIECpibz/Gdwy7hynYGXE8xVu6wvL6OksrPVQU1ANTRtJQAaYsmE8GHNy+ybX+NlM3pB214IQCmusKumFMiCJICkaDMf2jPnmeI5RDpUKkciGKyaTAupLWSgdPa8osJ5vN0ZMpjtF0wwaNMKIRNyEMKOcp2gyZTtMTOe9gkBIcR2LKkliErLVOcTg0PHBmlUbk8cC5szgCTp/axFQle3e2Obu1yQ//4A+QZzmehOnwiMgL+cTHPoqg4nD3FmHcqPuDtT7x0JGlBX7oUVYpvt+sawBFxf70gF6vS7vdwWiLMRAEHtIpabZC0mxK5HVJZqZ2PkIghCKOIypdgBV4oUdeWXRW57uPk3QUVoOj4HOf/mXM/i3O9Ja4cThlMh+xGXdQUhJ6CplrpuMp16++ypXLDy+etOua0WQ8xyJJkhnaWJqtFayojTTUt8DOXl1+oE7ROBF+2ODcA1tMZrvcvnODyXSGpCKOYtKioNNoMU0maKG5s3cLS87m5iZ5PqM/GrK5ep5TW1tcvHAB13MZ9I+4fWubMPbpPfauY9extBphEEgl8KSDJwSD+YwsTzjdaPFf/t0f4dzlczz13LO89vpTrC6d4vs++TH0fAZHu1TzA54ZjRiMdyn39+kf7DKYjFna3GRzdYkXb9zAUZLTq8fXkO7q7c2J3x09BayQpOkRX/nip+n397GVwMFgcEjLnMANkAiUE3Lp4ceZj3fBGEyp6U+G3Nw7wDGWojQUefJnKpp6ro828OrV12jFDRzHQQhBvLHO0vISQgjKqkKb+pQSRS5yYcJgjOHo6Ig8S3jHo+9gb3+Pq1f/X/beLEay7Lzz+51z7hp7ZEYulUtV1957q9lkb9wXQeRIIqXRaBtD47EfRpA9kOEHw2MYM34Yz8BvkjAGbA08GEmWPFpJbSRFUhRFNtXN7mbv7K32yqzKyi32iLufc/xwo4otDTuzJRskBdSHLCCRiMr4Mu693/nOd/7LJV584UU+/tFHIPQx1lAUpZej7zffNo+bBVwIgT9zqPE8r3SqyXMuXbvGS5vXcId3sh3ndHPL1Qsb7O5v02nMUVTqvHjlGtVhhBCl4Hy93qBarxGNp8TZhGma0Osf7GZuhUJIhRQCXxQ4NkOPd5lsX2ayfx1fOCjXIcsNSZzTnU749d/5HEGtxvrpde5+8CynTp3AVz7T7h7jXpfBfnlzDocDqne28VtVDgNlqCyj7ge0pU9ucpQxeFKCUhRFQTHqgRD4aQ0/DFC+ohZJQs/HSBij2cojpM1puj5FkjEaTNBxSm86xdiCPDVkxcEHaNiSpWuspuIHrLWO4Dp1drcu8cAPrBPHA1qtKo899igf/ehHadbrDAc9Go2Q97znXUzGIzYuXmQ0GLG8MI/nOoxGYxzH+Rv36cGFy1IWTs8Dz3NpNeao15sUvqZSqdFoNHEdn73dIdVagB/4XL16hThOWFxcwXNr1OsNzp3fwxqHNElRrgThYooCJRzG/Zj8EBJWeQprMPGEN5/5OoHRbE8NN0Yxga+IU41UBaK8VKUh84Xz5LrAkZZBt09vnPHMU88SeD67vWvgSBrzx+gPZwuIsSXE9YD4hZ//FwSBR+A18bwa0pmwuXmOV7/1Go3mHLVwkWqtwg99IkcKh8CrMBnGDHp9Bts7DMdDhv0EpapMJlNubN3gyPIKjuMCDkqVu9I8O3g4P9+poBUoHxZaLnkvZjzsMY4S3qPHfDx8jOmxDr/1m89jR1N25AZ333eC9Lc+w+APP8/g7has3s3mtT4f8wNemsRsb+6yt3mJj33kYYosBRzuPHPi4Osyi+9+Jz4r5ApBtzvgz77weZI4QUoH1wtKEo6wCFfhugELrQbu6Dyb2+e4tnmZNEnY2d2lsC65cmh3miTxeOa3985CIMmzlP5wxOrqEbTW9Ho9lJQkSUmqqFar5am9teR5QZ7nCCGoVCrkeXna/rWvfpUXXnyBlbVTPP7oe8AaikKTFhlZlhFFEe322xfxW05FnneriAMzpqXDTnefZ954nfbjj6MrHby1u2j3JtgkZdLbAZszSXKKbIyUPtZq9vf3sLooHYvQGGsO3abiOCAFjjU4oyH7Vy+y89qrjK5cRo+67OUxo0gQegovdAmkS71Wxfc9/HQA3W1qx9eo1xu8/vqrbF46z6C7X3qj+h6J6+F5DsI5uOMrbOmolCLRRlCfa1NtNSl0QZpkDAZjsiwjCAI6nQ61RhWtc1yhkC64UpRooljjOJY4KccIwvhIJWi3G+XY5xAUBKbsTiu+z1ytSavT4diJuylUwMJCjeGgh+9KPvKh9zLXrBJPx2QFhEGIFOUB8XxnGW0kjXYLJS15mqAcFyUk+cyr3R5SxD3PIS9SlpaXyKcpk2lCGFaptEP293vUajVym5OkE8aTLn44x2gYs7DYoTO/ROC12NndmFHEWwyHPTwR4FVrREnEfK1BEY2IkkPGS9biCcPzT34FWcTsZznX4wS0ZLU1x6W9HdqBiysswmiklXz+s5/lx3/iJzl+dJEvffZP+PQXvsHOTpdkmpClE8JKlbDSJMkkRtvDzlYBWDt6N1gN2scKD0PAsRMtjp/6ACaTYByEA0IVpaRGFNNqGQJ/Bz2yvPTieY4sHmd5bRVRFCRpRq1e58iRVYQ1dOZCpNDs7uwcnIhImU4TsoHAdTqozIKUjKKYFy51Obv3ab555TIb16/QWj6N3t/lpadeoPWXT1Pp9UnPGb509U2y8ZAfW2wjwgaLhHiVMfOtCmkER1fmaFUPhuTejO9uJ/6WLsSgWVg/zf/0v/0u//6X/gcGvU0QBld6uK6DLnIm0YhiuMX/ceESKFDpBN8KxjrADUsNDFe02Dj/Amfvfg+15jvbfkgh2dnb5+Sp04xGfWrVKp2lTulRKRXb29v8/u9/ho9//JO8/PJLDAYDfvoffZInn/wrPv/5z7O1dZ08iXGV4tixY/zyL/8MWZFzfWsD78RJ8iwnz3OK/BCXeTHDZ1tbjl5kCaUL/BAhFJ15ePlrXyKYW+bhH/05vPs+SMu5i+qjFiV2+L3/89+yd+VVlhcy7j95morvcenSOS5u9GnNtzi+epQoTfB978A8HJPj5hOKfo+n/+D32L1yCZlnuKIcPbWaCzjSEjhQccC1BUQDHOPQ8AP8oc/48hvk7RXqgcNSp00STbhyo8v2xLJ40qUiHZxDWMQ7SU5/2kNMphwNFc1aOcLq9YbU63VOnDgLwO7uLlkOWvqs3XGKIsnIk4RKkTM3v8CJEzmFL4l0wfwdq9hqQFJoiklBbjTFIXMMQcmg03lGq9YiTjP6oyFLi03SaMDVi2PiaEpa5IxHfWrVBhc2rjA/N8fK4jLCWlw/4MSpM3hhQL+7TzSd4LpuacMX1GbqYgfnsbayQp5GBJUK4VIFN3C4fO0KzUqDUX9EpRKwvLTC0uIyl69UuXTlEs8/9wKNRo3Tp89w/70PsTcY8K3XzvOh9zzKt65u8aEf/GFWjx3nwsULzNdfpT/os7hyMJN248pFfvNX/jX60rMkjVVef+MaTpqxXBNM44RhrFmuV+hUXMbJhGkmufDGG/zsJ3+Mf/Dovezu93nt9T209CmyIR9632P8wn/zC5y6+x6+8MXnybKUIDy8YG1eK71ApUiwbrmzVwhskTAd7XJ1LDBxxHpNUq9UeObCeYZ5HycaU6tW+cCnHme+UcURhs/94df4oX/4KRzfJc7GSFsQtqtgYb359o0XUGrwdAUVv8LW/i6WgmQaMx3HTO46y+jn/mv6zz/PQ/sx61sv8fJaB1Wt8a2f+RSVoofXj7jxK7/GI2vr/EUg8EjZGm+x3qjhS8OpM20ePnuclbmD87gZ37OZuLQSo3MsDmvrZ9jb3sTzHcJKk4XFVSq1OpVKlWe+/mUWVxYReUZhOuVJd5IiFbiej9Vw+eI5Ll98hfve9aF3+O6GSlhhe7dLtztgb68LWFzlkGc5y6sr/ORP/gTVsEHw7nfx3HMv8PIrL/MHf/B7DAZ9lBC41QpGa6SjGE/H/NKv/DI//COfYHHxCHGUkaVlJ378gB3RTSZqURRIIXA8H0c6eI5PGNQQrkM/zjj33POcfM+H8RbvAAtxlnP09DEe/9gP8aeXz3NjZ5PlRpOHHriPeu0BXvjWiwzGY8aTCdM0Jj+k8/RMhu4PGVy6QL53HRlPKIyhcFw85ZJlGZnJKITBepJQQYDFcRw8TyFFwXQ6YKwlo1HE5QuXuHh5k15kSd0WHW2xRYE4hO7e7fbwlcDNczIccD0qgY/XmmcynRKPx9TrDRqVCq7v4zuK6XSITnPyJCVLUrI0J9Yag6Jar+F4Hl61Ss3xcZozAaVDuj4xwzI7jovWtsQRC4daNSAa7SOsIImnDCdjpHTIsoyNzQ2sMbRqderVGkJIsixjOBwynY7RWhOGIViLchT2sN0RcOXyNVaW5sEIzp69l93dHbKsYGt0g6ofAIZLly4SR4ZpNAUUDz/yML7nEAQhRZHYyi+lAAAgAElEQVTT6SxS5PAHf/xZjp44Tqs9T5rEBEFAozVPevnqoRDDr/3l1/nSl7/EPYtV+skeo/GYRT8kM4ZEa9AaoxM8P8BzXTItwKb09nf5T3/yJZLcMjUObhByx7Ej/OzP/SyPPvYeLm9dJqyWOHjwecu09TuGLmZkPylAzli1xiAMWK/Flb0rFPGQ9bUzXOuPuTHNSFAMd4fU3JRBBIE74OjaCq3OGsgqwvFRsmQ/G5PNpl0Hj3UazTrKKtI8ZOPGBVRFE08KjHKxeUR/MOCee+6id+FOmnnOfY88xLA7oFOtkV96lmDuKO3VVfYHW6x5hjYp9338MY7M16gHinfff5aaF5K/M7Ddd7eI35Q5LTsdW+LDbcGHfuQfc/+jH2awv0WhNSurJ+gsHqXRaKBUwPs/+uO4TkBe5CAsWZqQFxmVSgkVU1IgnYO7zbeGtgVPP/00ftggSzXaZCglkBY+8+lP83P/1T+l3+/z3DPPMxwOCcMKxlpa7RZJPEWYAj2DTLmex2gy4cKFS1zbvEG/18dolyzNiJNDDIopDzC/TQxSYMt/nhfSaDUpjOXi3h4bb7zMPYtrGMfBMwW5ltz38Pt47kuf5+q5F9i4cZ1mvcr6kSO06g32B12MMfieTxCGBycRT+hdu8re+dfxpkOcPCZHkiKIdIyejKkELip0yXONLAyVQJWQSKUwUtAbjbh47jpXru6QxBlxKohshWHiEm7vES50qB0wWoKSpIIrUZ5COBItIdU5WZET24K9/h7udEiapgSBT93UWA0FeZIwHo4YDsbEsSHSBulXWF47SuGV18oajS5syQA9BCcupEAi8X0fz/VJtEEpF891UFLQ29lnMBoS56XIWZJmzM/NU61Wb52FRFFMGJY7CcdxShlcYxAzspGQEnnIOc54lNBVQ+q55YH7H+Hq5ctcvPg6wjPMdRogLPu9LklkUK5Dtd5gZWmJOBoThgFpmhAlCfNzi+z2x4SNNr7rlOc+UjAcJ4zHGYU4eAbcaraxTsj1UUHmZuC67EynZNqQ5hbp+eTaYThNyY2hEAYXi3AkuaqgheHd993FRz72Ef70c39GvVXH5JqK12SuNUYbXdYDIw7EGUrplnhzIbBWIyz4jkeWJAz6CZtXbvDmt56hLmKs9UlTwdMvvILMIj71o58i9D0uXbzAZ//iM5xsLfMD1sMYDwcBQqGFekcACUd4LCxUmEYu9/lnqdct7sk6GzsxJpfc2N6mVq8Rn70L6XkErXU2btyg1mhShGdxtOChe47RYpkzGwP0Qp0Tp1Y5tdrk6sWLZL0IvdRg/sjqobnA9wHtXkqBH4QcP3U/5vid5TycUiBmNB7x8U/9E7JcYtG4qizUXlBHCDC6pGyWi4HF2nJ8cdhlKHLN7/3u7/Kudz/G4tISCI3WYETB3u4e/cGAer3OXXedxXW9kvmWxxw7dgdb1zYRppxmep5HJazQqNdZXFrCdV2m05g8i8nznDRND/37b7HspCxJTkYCCmsl1aDOakcyjnLG25uoIiH0Q7CSOLPUGh3Wz5xlc+NV+uMRFy5dJI8iWq06nuvgei4o+W3m49tEMo2Y7O9jJ0OqrstQKLQVaAtGQA5IZ3ZmISyFzqlXXBbqDtLk9PojjK9J0oLNnQGOG+JV2ziqgh5n6DTB5hnqkPFBu1FHUyB0TmZ0qeiY5/TjMUU9IK2HKKWIpQCT4I9yPN8lEAIhBUElxKmETAZDGs05wnqTyMblIaWx5HmGdBz8Q4q4ERJrLLHJ8H0XR7g0G1UmozFJkhKlKaPJlChJSNOMVlOzsDiPtDl5ljDVmqIw1DoVrCmQ1uApiSMlVgis8JDSQYiD50tlN59TrdYJwwa93ph+b8D62jxREpMXklZ7HjnnohwXv1Khu7uDzhNcr1w4ijzlsccf49S9D7C6vEBnfo44jtBFxqWNaxip8L2DRxnLK/MsHjmCmI7pDmO8ap31s3cSRTFxmnPu3GU8x6UWBrhRjk0zatUqSaFLJKWveP/7H+fH/+Gn+H9+54+ZTIck0z7DnSl/8cWv8u53/1PyLKZ/5Q2Wzjz0tnk4ysFogzEWKLAWcq2ZjEZcefMioyubXH7+ZZ5zNGfvegBpPFarS8hazt7ekI3NTa5vbXHx6i4PnLifncGYRltSCyq4ysEWqmTIHjJuO7b6IGlS0KhaGnFGnKYgPRqNck4/HE2YTmMK47A9GlPbfApz5Dhqf4h0YK5ZYXF+nnQ6oXdC01aSjfMXkNuWis145IEWF6YTrgxfOjCPW5/LO3rV/5/xNwSuhZAoIEsipCpvamNyBGVhi3KLtNmt3t0CZeMgS3q7FdjZ8v1OYYZxnBKEIfv7+8zNz6Nmn4IxhkazwXg8LjWpFxbwPI9z586BFZw8cZIXvvksWRKX72sgzwuazSY//8/+GbVajclkijWqnIkfduo/CzvT8c5NgWc1+aybC/0QaTXtaoVpd48sHuH6bQIbMLUWG1Q4cf+DvPjUF4mSAYPpmOakRrNRp11rIl0XI0H7h+zLCoHIMlSRkAhIhUOOQguBtoKpVthRwngUIxG4juKeOzosLbo4riC2ith41L0ck0sSK5AVieMKqn5BxVGEjoNzCIzM5DmuV3aoOsvQjouREtloUFmaw/G9EvFTK6GKnpBMMs1kGuFJQVCpIURARQq89jyNRgsbjxFZitYav+mjtWXQP1gdzlLqpBcIJtOYpJhyY+s6/d6AQX/I9Z0dpFAUuUGgsBbSJCoPk8MqaaEJwyrWGqyxOFKiZ7BT5XhooWY48cO27Q18ZXBdycULl1hcOIIuhmTZgOk4IppOaLfmmJ9vs7y8RLXWpFltlDDd0KXfGyCEoT3X4NH3fwTHFEwnQ/Z2d9jvddnr7xNU64ceKgoxorM4xxsvXuHEqbtYOXaUj/7gx6jXK3S7ff7n//5fcHrV495T6+QvXCGbpDRDH6/QTArDYDjiD377t3j5pWdQ2jDpX+eZJ6/zJ3/6LBc2x1y+8n7ia1fpnv86nzigiBurZ5IW4LoSaSyTwQijM06sLNOp1Xj/Xcc4fnQZt1ojqHVwwhrTdMJuNOKOY8fp9Yfs9/rcub7GXz71DfrjEasLS3zo8UdpNWuI2fscFH/+lTe50R0TZwlZYrBYcpPjOYojSwvYmZRtYQry8Zj2dJ/82Dae69Id9Ln3rjPceeIoe9GYbSzTvT7DniGdq7O2NMe7HzvB9lMv8YUnnjv4wszie96Jl5hqByFyhNVYK1CifJCgLNXlt6WJw006u7ACgfxb66aUITi6fozRNJ45+thbh4udTodBv8/a2ip5kdLt7tFo1lg/dgeukgRBhSSKyIsCpSwnT55kb2+Po0fXefbZ5zh27HjZUVuLPKTTgrcqNFqs0hQiQ5PPFi0YTScYbRh0uwyHe7TCGsVE4FuPFIeFM3dydP0Ur/eeYZTHDLKIpbygFbjoHKwwt1igbxcuAmksRZqR5TlagFWidOwxEEqXpq8InFIOIdcF57d3qDeXWW8vUpc+070R169uUfOrFMIlHoxJ9nvgSMJqBS8MSrTKARH4Pr4jcYUkdHMKAbFS6FqdFA8dJ0TRFN/3ybUG10eEVZLhlGg6RU0ybLWGDQLcaojwXJwipOL4GG0Yjyd0t/fYvXEw+kAIgRQeYVAnSi3SddncuMbrr71Blk5n45wAawStdgvfC/BcH6PBGEGW5Rw/vkyn02Hj6lWyosDzfQwSawVSiHd0277roft55YVv4vouS0vLDHr7DEd9kmzE8sI6KytNojgBRxHFMd3+iBPrq+TZHHmR0Z5foLu/SyXwmHR36Xb32e/tsL2zwzeeeRZtIUmTQxfX7t4rPPb4fYRuxPs/9BhxlrO/+wbStqkGkgdPd7hrJaDuxCzVA7qeR+CWpDiNphH4jHa3mMz5HJ9vsRAWtOoOV6++wsb1lN/61f+L1XyXZLDDJ37+7fMoNfpBOQ5z7Sqe69LpNKHQSKvQaYbQBdF0Qi8aM42H7A16xPkULaDielSWFlhZOsL161e4un2dq9c3uXj5Is2qzw9+8IMYo5GHCNu8/MZ1tJg1aNqgdVk7ImPY390jGnQRgDY5Ujo4QnHKu4brulzauEYeReTTjO5+j258DRH1aLh1ruwOOLc74N3vfRA/D+juH4KimsV3HyfOdzJ9MHBzs/03nGiELfVIbjpiYMvu/e+kfDWLfm/CmTNneeKpJzl+/Dj1epXpdER/v8vJkyd5+ZVXeOSRR5iMRvzLf/mv+OQnP8nj7/sw1gg++tGPMR4MyAvD9s4O/+E//Br/6bd/m8FwyA9+7BM89NAjxFFKUeQEQcgnfuT9b5tHabNVrvpxHONaD6VctCoIggBkOTtday/gGkvRG1O/u4IOYqwQpEXO3PIdHH/3B/nWc09ijGFnZ5dOUOf0kXXG43FZxA9ByUyEJlEW6yka1Rq96Yg8L0kaQgrma4IjFUHNmS06KuShd9/NsZUWuYELmzu89MZVtrZ7KK9JWKvjSokZD3A8hzP330Nj7QiycvC5RX+YUZUw5wiUFzJ2PLo4XN3qYbKEk8eWCfw2u7u77HX3yYuCXnOOIMk4Nr/AqePH6eYp14cD9rd2uDK4QH88xVAStEajEUoqUIcJYIHjhDRqi5y8+yGGvT1c4eFKn3Pn3yCaTjHG0mq3UVIymSR09/eZn5+n3V7ixo09Lly4yNGj6ywsLGKBll/BczyUN7OKO5woSVZE+KFDfzDgqae+wuUr5yiKmNOn72I8sRw51ixz8BVCKQZ7+1zbcbHWYRKnhFWfxsoylzev8kef/Ty90Yi93W0arRqDSYREIkxOa/FgyeQPPX4no6nD+x49w8Kig9WSQX9MpeLRaNQofvRh9i9skWBZOSIZZwUOoG2AEimeyak0VviFn/oRrp1/jvtPtPijP32Oq5sDNm/cIN5ss3bc54k3Nw/Mo9AZUgiKNOPFp5+nMDFZkTGNcobRlCSPUY6HkE45m88t0yijMAVFVjZFhYEoSdjqDdi4cQ3pCJyK4qvffIIPf+RxhDWH3R588L1305tM2d4ZEMURotCgDVLa0gxkPsD3PBxH4roeynH46Iffg5KS8Shirx/RHUSMs4zrmzEf/cAHke4+DC4zTvf53//kP1LIlIcf+T5Gp7y1gP+tXXn+jkYQb408L/BcD8dx+OpffhU/cJmfb+GpcrFI05Rur8dco836+h1cunSV02f3SNOMNM25du0a3d6AzsICIOh2ezRbDebm5tnd2aMoNJ7nUasdLLZ/E3uunJKxKShFwdzZPHs8GTO/0MHPBVcvXGG61wUXwpoinhZIXFAui2t3EIYV8iInT3OiJCYtclzHAQP16sEQss6RJSrZCfYmA9LoOjXPA3Jya8mloJtDNoyoUKCsRUpFc2OP1vwig0GXK1c3mU7HhFWPOIkZ9qcYKfCqPnNH5vCqIcpxkYfAQtqdJqF0qCmBnvaYRjF7UcqN7T3IU3xVasqMJxNAUg1qNOp1xuNtBpMRw+mE/eGQQTzl+mhMb69PmpYemFJKKpUKQgiK4pADZ1E+kAoXP6jQml8iiRL8Wo2FpWW2Nq/SarbwPR9dFDgS9vcT4mSHH3jwEe5utHj11ReYTEb4fkC92Sz9txBISljpYSxJgGvXruG6Hnmes719FZwcVzmcO38B369x9sQdpFHKxpVrSKWYa3dotBYByY3u6/zh5z8Nbkbg+OiY0jQkKYiihDRNUVLjV1286iE7Rlu6YTmOQOJgBFRrVcbTMWG1SmthjqsvvgqVCnP1gIVWOVZURtKuV8jyKe35edqNCu6xIxS6YGu7yySOsbnmkUceYLj5OpPJYRLBEmNBOS5f/sJXmKT7jJMJhXVJtWUQjUnyjLzI0VphDWitS6dB14OZm06hCzQO1lqUEuh8imy1yAFHCJxD5ksP3HWCTOckSak+WDoOlRMyi8F31IyFbUFIrFZIVUKbmw0H5Xh4fkC7VeON1y/x2utX+OjH72PuDsnu4DqXz/cY9AXp5PuwE78Zb1eIv5P5w3d2+/nOv+ud6oznWYEFmo0Gd955J/vdXfr9Po6QTCYTlpaX6HX7dFqL/KOf+Gm6vS6eF/Diuef54he/RDKdUK030KZ8MBxXsrq6xnA4otctJQCazYNFlsrkS0SGlKXbka8cXCmxJmd79wpZHhHHTephBZVFVEcRmQTfEVivIGj4CFtQr4S4rk+UphSmYGfS49LuJnceOQ46B/fgh7QhFNZvEesqg3FBZ36epkkZTAdEacLCfIdObY524BFKi+8IanXLYOscw26fjtQcu/M4qQSTS7rdPv1phGo1yALBJE6pW0UoDu7EM50hgbERNIWkyDNGwwGTyRCbZUixhi40wjo0mw1q1ZBAKnI/INWa7e4+ozTFOooEgwlUiToy5c5PeLPOyBymYmhAGAQC1/VwHBfHDVCOSxAEVAO/ZHXmxbfPM4Aoiun293nkkYfxfNi+sV3KETguzuz3yEN8Rv/a55GmVHyPNNdcuXaJo3euooQi3R/Ti7YI6i4bV6/x0kuv8L73vQ+lSmbxZDplMOgxicYsr7dotxbYvtxlPJqipEc0TRmPx7QWaki/wA0OboyUkEgJriq3D3JGehtORmR5zvzqMgaNSCKq1XladZ/BYEpRJISNJq7jcPzkcSSGdrPKZJIynCRE05S27/HkX36dNjFN93B1yXLUJVg/dpJLGznd0YT+cMBwMiW1JQ9AW0Ou5exso1w8izybDSjLcazOC7QxpU9uERAsHGHYn9LwQg6bghqrUQiatQqF1rf4HtoUlFOFUkStBFswO81zylGacqlWJI4XkBeaVrvBpaubiC8aHn3fGVZaAdFyRlgpWZvvJL5nRfw7+WjeLMJ/l277b2MU4YcBUkp8PyDPMtZX1vB8B6sNzz7zLJ25Bbr7+8RHVjhyZJFms8qnP/MZnv7GU1zf3EAJgd3Zo9CGWrVGrTmHUj6bm9ewVtGZn8dxFYU+xIxBzVTerC1xyUqhhCDPU6JojCNd0jhnW6fk05zejescsaCVQpsCX4jSCm4Q0aw1SlnLPC39JK3GC3yUdcv58QGRjMekcUKcabYGY6ZFgR+4uJU2zZrFdQWTKMamCa3Awa/5LDbqjIZdqp7Fkz6j8YhxUWBznzxReKpJXlTY2OixPByztKIPhdT1pkPyOEVEKe89vk7oSxyRErgWx6+ycuQovd4+URQxjqYYa1hrdNiJc/o6pfBdlO8zynOkq2jOVah4AZUgxPd9HMclKwrS/DDop7jlQu65PoXW+H4p0OQohSMMRZYxHk9mnonQ0BntdoOd3es8+dQTtFotwmoNz6tSrTVwPB+pXIRyDsVD3wwLRFFElORU5+oEcyF5mrJ8fA63Irl4/RztzhLNRpNvfvN5qtU6/d4+f/7lL5eMzMUG1WrAfm8fiSKaRIRBDYsgiRNEUKPa8Am8gztP359HZjnKKWaKmBLHcak3C4pC01haIWg2yQYDfEfTqiniJEcIqPiKdqvFwtISeZEzHU95/i+e4RvPvUKWFdwxV0HEI5Qn8bx3doZkjOG97/8Yp/fvYjjpsrV9g62dHb715huMogm5LpikBbnW5FmOtgVa57dMxZRSFOkEx3XxXZ9WvUqn2SaJMkLho+zBeZgZOq3QeuZqLyiKAmM0jitvjUi/3YDeBF6UozopJY5TCoqtrCzQH445d/Ea3cGUe+85xcrJM6wtpUh1OLoNvocHm++ks36nv+Nv6/QzjSOkKkcN//ev/Tpnzpyh1W5Rq9YYDidUKhVee/lbCJNz8eIFXnnlFZ5/5lmEAMdxMDNBn3qlxvLSEl6lClLhOA7VapW5uXlct+zaDou3LlxGGzJymI1ZsjRBCofucJ881uSXL/ADWJQjaDgOflGqKI539whcj0alRjqZ4CDwlEMQhlhtmE4nB+agswIpJW7gkZiCtN/DdxW1aojnSXSUQl4wtoa+sHR9Sachy9dUAoaRZjIYM0xyJoOY6SQnMZLUC4hDn3Z7rtSnOOTapmmEMaXRrd+sUJM5/g2wSUxlvkmWGs69eYlCp4S1AClLMayhECQCmp6PkhKrC6bjMdVQETbqtNsNfN+fbXkt+hD0wVsXGykdFBIpHIRfPi5W59igwCBRshyFGWFuFQnX9bBCUm3UqIQNwloN5bil47xQ74hQAjAaxOR5glAWR3hUmhWWOidp1puM0xtsvrDDybvP8NC73k2vP6TfH3D+zdc4sjTPo4/9KGme8syrX2M06WMjl7zI0NG4lJUVijwVKNchqB5cBpzKEiqZ4IaSoBoACuU4zPsthvtd5pbmWDx+mje//gTteU3VM0RRRqsVIK3GURLlemjp8PmvPM1Xzo+4vhehraARBviiLHTyEKjjzQIuhED5IdVmh6TIWF9TdBYWOLK2ynAyZjAeMowT4iwlmkYkSUyWR6RpitYFcZygQsni4hHm5xZYWz3KieP30ajUSw7BIeMUXVgsBiu/jRm4xfcwYuYp8NbE36psCtaIkjAoFffddxqlXM5fvEK3v89TzwxYv7ZGZ6FJs/X9qif+Hbrwd1q4Dyv877SWP/3000ynU15++WWeffZZLl26xHg8LueESvGBD3yAJ574Gp/93J9QKsAJ5jorBH5ArV4nDEN8z8dxnLLjqlQJq7VbYlZSOrf0UA4KTzkzVEy5NZ/mupSuFaWZrgHcNCFOU46uHUMgePmPfp8P/oMfZiGxsN/n5eee5NXP/x5xOmY07BF6LqHjUnV9xr0e43hM/RCyj3JrrB3tsNRu0rF9vHSEGXaR6RRXGsLWHL1JRm+cMU0KumnBC5d3OLbSwZUF3e6Ird0+kyJnbu4IS8dWaCws0FxbYeHUScITp0A6OIdc5qVWEyMsWZrx2s4NFlodHrj3ITpz+zQ7y2S6yoMPPIwRGXkRk44n/NXTz1BdaLO+uspyu0VDSeKdLY4eWyd0JZVKiKOcsqiGNYQ2JFF0YB7SCLQEqS3glONsCRKDVCUJCGNpzy2XomjWsLC8NluINcpxEE75no5ykY6aLWCC0gqkPFsQh9ywVnvEcUSrE6LJ2bj6BsfvaGFkTpT2+PMvPsPll/o8+J57WFtdY3FhmbByL+cuvMHvfPp3sNIQNF1WVlc4srDGE1/+K3RSIERphl3xXOqNCqvHDparsI6HUWA9H+m0y85SSlxhmF9soYHHfuy/YOPNC4g8pxV6FEWGrwKKeIiDy+tvnuc9D/4YL10YsbGTU0iFNTE7Ucox24LCYA45gH+rxPNEp7hhhXqjyblzb9Lv73Fte49pEjGeTkmyjEIXDAZDQt+nEVaZW1yg0+mwsrLC0WNH8b0qYdDEdSq4XlhCmAXcHIm8XRgz07lX5RilKIqZHaOcYdjLxfyt+X77j5AYU87OlZDMtZq897F7eO8jZ5nGKXv7I27s7DIZT7mxdbBw3c34nmmnHPSzd/p/v/PrDi/mWZoznURMxhNOnz6DMZq5uXm01mRZxosvvkSSpHheSBiGVCoVlpfWCMMKvu+XrD9PUamELCwsoJRDXuhSF0MpwrCK53n/mV74f5ZriSxEQqmzokpcNlAezlhLPBwSuCF5njPXrJNvXGX7+ddZcOZoAmprj4or2RqOKUwOuUb7ASYvOHXiON1xn/wQm6dCCVJhMUowiKfUKDh15ylW5hqEUrB/bZdpskNnsc2dq0dZPbrG8aNt0nGXve1tZL1HdWWJ9sISi+unsMrHrdSpzy/gVutMHAeEOlRutFHvUBQFEz0htoJepnCFQ4rP/v6ESk3huoJKq4Hnt8jjmGarwiSJiNOI3kRRac/h16sQKlwhEc6sC9SwvdOnVQkQh5gxYErCh7DlNPPmnSeERUmJwcU6oLHYmbeZeesocCaCdbMBsJZZwS47OIMuczjkcHMwGGAwjEYR8+0GptDEcZ96vUK1IpHKZTJNsLZAOYJskrB27AhL0yEnTp8gyadsXtvkwutXCJwqjueSpQWCsknodSc4Ncn8wsFlQFiNsjnWFgiTlugxU3afUirG/TH1+RbH7r2H7PplUm05fnqVxfkWjgDtBnRtjl8LOHlmHToxWkkcLPOBz3KnipI5xSGXRSk1c/GyWGkwRpBmMXkeMZ70mE765NZQqbg0GqUEwOK77qMWVmkGder10hgiDAP8Sg3XqeI6FZQKUVKiheZwRZtZ1z2Di2pdEsluaiCVn4n8a7sGa0tno5szcso7AwBpLViFtYLAESwtODQapa1iNH1nPJPvq4PNv9Vrv0O1lmrW4R9yFQaDEVEUc+rUWfrd7q1t/k2W5YUL57n77nup1aslisV1qIVtPD/A9z2qlSphxcNx1S18uVLlOMVxnBIfqzX5IbNX13VJ07S88KqksdvZTSqlJE0TjDGk2ZStG5s4QnCiuUz/ueeoHLmH5bkOK36VwPcoCn2r2XNUiW4ZTsrdRWmr8vbh5GOKiWS0v0OcaT74kQ/zwJ2nmW/WkFpz4/wl/LVtFtaPs37qNI12C6HHjLo7dI6e4V4voFpvElSqFKacn0dRSqYNxXTIZBRhhcARAupn3jaPNClnjLoQaEex3R8QjWN6e31MVnD/3WdozjfLDkgpqq0mYavOcDKm0JrQ9zFBjblqAGFpDDHY22N7u8tkFJFMc/pBSOUQp6NqECB9H2neqjN4c1hiyy9BiaefGb1a++1yLyiFmYQV32FqIsAahLVUDtFXV2pm9YfAd3ziqWbj0g5zcxUyEbN+xyKjGxM2NjcZDhParQ4bVy6wstzB9x9mt7fLpJewc2OPzYubKEqBtTRJS6G1oEKWwdVLB+Pmr51/lSiagIC4u01e5KXl2oyur7Wg33VZP3uGYmWRKNf86FmD63lIURpwp9aQpzv88I99kHFuKKzAE2UjU5WGvEgRzmHlSMzgxgZXCqzOUGR0Wk0qvuKOY8eRSqEches4s5Gmj+u4OMLBUYmMi0cAACAASURBVA5QPqc4NZTyUaocnxhbMlPEOzyxKBdmgZk9qzfr1E3J6psNnNYarJjtfuxfmyjedKQq/7RyYVXConKBF/ozu8XDQ/x/dY6/HbfjdtyO2/G9i3cuwn07bsftuB234/subhfx23E7bsft+Hsct4v47bgdt+N2/D2O20X8dtyO23E7/h7HdxWd8sQL5+ww0pAXxGlGnuclGkHrW/AcYwyu5+I5CldaZGZI84JE5+RGY2WpYiYQoBRCKCTgSQeEA9KhQPJf/siDb3vE/PP/6y/bQoUgJJPJGM9zCYKwJHlISUhKnOWMC8VizUHZHLwGTp4yiKa4tXp5hi0kFonRJW40z3MKNNU0ofBCTGH4j//qv33bPH71V37J1mo1ut0ujXoLKeVMwtayuLjAa288S7U+x+LCCpvXNlk5ssLO3j6+55NlWYlX931832d5eZkkjhkOh7iui3IcRsNhqb89jfj5/+6fv20ev/iL/9y6rovrurQadaqVAMcvZVuV6/PgQ49y99134gc+W1s7vP76G5x/9VtMp1OSNCGOYow1KCXwPJd2u81zzz3H1Y0Nzpw9Q57nRFFEUeT88R/+8dvm8b/8j//YHlkO6cy1aNePMBz22Ni8zgP3P8DS8hLtzjzjSZ8onjDXWkLKCrkSJEnMxYsXqdXqnD59ksGwzxuXLmCExWiBKSTtuSqtdoN2YwEdTbn7/k+9bR5P/tvPWZOWhKvmwhzKGBwpMBJyDKKQuK6DUxjICjIBWgmsEGS6KA2CNQRCMdUZg2zC1eFVPvP132Srv81gnIOEwhiu3ei9bR7/5t/9hl1fX2FtbZVXnnsaz5VM4imZybh48QLXb2yBawg9iTIF43GEwMVouHr1Kvfed5Yg8EjSiKWlDghBJQxZP3qUZ55+miTJCCsuc3ML/Lt/86tvm8fW3r71HInvOvzmb/waD73rQe66516STOO4HmKGqLrpGXtTiuDbsDrLTWFArTWu695iOQLESYxVEGcFZ9fvePv79I9ft1LPVDlFaeKibKl1IqRBqRnNffaeWEFhDVaUeCFryp8baygsGG4i3EqSjr6FJYVf/al73zaPf/3rX7eOKgl/jnJuoVOkEKWEhiMRwkOajMKIEqKpHJSQGFnMUExlLuLmB2PtLaq+NjfroeUXf+rhQ6Ey39UiPh5PSY2LRGBNhpS3FMJJMks0jUsQV5Qg0NRCn2g4KdlcroPjuVhjSZK8/MCU4RYqSQmEEmhtMYdISWptsNKWxBqAmcZCkRckaYQXCgptMLgUxmK0Qbk3AWYlljvXGtdVTCZTCm0pihxjLFmRoqQgIyVLDhawkVIyHo+Zn58nTTOyJMHzPFqtNuPJCGPg2uYNBoOUyXhMNE05sthhOp2Wr6tVStKB1Vy+cJ72fIfJZILneYxGI06fPl1ijQ9BIDmOc0v/ofxe3oJIua6LH7iEYYAUMJ2OGI8HGFsgpCUIPISwM6hkKfwlhGA6nSKkoNFosLu7O7vKB1+XZrVKs17BVZJBdx8vqOC5LS5c3icyDrG2tNp1qk6AtgFBMEcc75PlU5aPLNCoLdDdHyOVwnVDwkoFx/GIxwmjXo8ji4tMhgNGe1e4+/4DErHgKFXC1ZTEUy6uKCGgSlhiNaZA43pOKWilwfUCclOQ6YLA83FMSRaSGgLXw3N8BIokSRmZAqMNHGJXVxSaooiJkn363Q3+6omvEtZDvIrDtWubOH6VaruK71cQmUWYgkc/8D7mFpd46qmnkDri9KmjFEXGlY1LjKOYldU1XnrtBRIT47gSPxA0mgdD2cZRiithrlHh2W98la2NcywutJnvrJbP20wu2mJLuWgLYqZXIwErJEZ+m/RyEz8tACElQRCQFTmuOkwy2aAkSEqjEzMzm9amhAYqqcr3vgkMteaWD4Gg+GtQ2zQXFAZyYbBiZvlmSxz6YcziwFNIKVAoHGdWwOWMqSkE6pu/Qeb6nPrITyMcjxSP/tiQmQwKD0sOUgAOws6Q6QZKMxiL1vZWEX8n8V0t4sYIcgvD4RCpU7QxWGvQhaUoYBqXpBQ/CPFch1wbhO+jZMlsNIXFCDFzRBFISr0Rx1L67JFjrMByiPaBNaUFl5S3OggpS82D0WhERQREWUHuKOLYELiAKbG/CEWSFURpSuBZbuzuo03ZHVggzWJwHYbGYouDH9K5uTl6vR57e3tYq6nX62hbsLOzRaVapV5vsbDQYDyJ8FyHtbU13BmmVsry9qzVaqRJih8EFEXBHXfcgeM4XLlyBTVTZewsdg7Mo1RcK4u2nOm5CLiFbfVdyWjQ5Y033uQLX/gzLl++jM7LjmppaYl6vY6jQuIkwXVLT84kTXAch3q9zs7OTik0dMjD0V5ew61ZeoMu9525k0a9iud3ePKZ1xhmkqWlOxiNNIPhHogugT/Aygw/LKhUaoRBBc9tEscxx4/eS2E0ezt9dORz9vhJJsOrFEWO9g5WdUzSBBdJ6Lpoo2f3nFNKt1qDkgJTaAoEPg5SgjZ6hp0uOzJtNWamiq8QKOGQpYbReIL1ApQQ+P7BOPHt7U3idBcjVrh8+RLGQKNRo9oK2NnbxDIhjVOmxMjCkMRTbly/ynynzdrKMtPBhCQSPP/8y5w4fQwrR4RB6fMZhhUoctrtJqsrKwfmEecW4cLefpdms8E3vvENtnd7/NTP/BPuu/9dqGC2M76JlZ5B6m/1UuLbJBdn1nUJIW7py7uOO3PKOvjzCBRYrREInBmzWSLB2Jm9m/5rjYJwDA4CjURaXeYwe05dWRJwSpsJO9OXLNUrD+OxeDPVUSXl7NkpceDlTkTy8usvogvD5770FywtL3Dm1Bl+4IOfgEoT7QbkVMgyTZoDNgfELZKoNRYluWXn907iu1vENURpRm5hd2pougHDfg8jDVUlcIL/l7k3C7Lsus70vn3mc+6cN/PmUJVZIwqFGSBAkBAIioPAFim1RMrdsiPUbnXY4Yi2REsvHjocDoUV4bD04PaLHT2E3G5ZkrtJqSlRFCkS4ggQxEygUEChCqi5cs688z33zHtvP5xbRSrCyNSDA+EVUS9VWXFXnrvP2nv/61//72JKQZxkTCZp6UBdrWHIAiE1hix332J2PbEtA1HkGKaFIQwKnYNhcJjt1Xg8hqT86kzTIE1T8jzHEAae72HbFjLOkKZAYdAfDMCW1F2bW1tbFG5AJvWdqSxEOa5b5AVpLpkUku1oinXIjcD1Amr1Asu22NnZpFar4tjl9Www6HP27H2MJkMMU1KtzTMcdImynEq1Sq/bxfAC3Oocm90dOvNtsiyj3+9TrVbwfY/r16+zsrJMnB4ysVkUWFb5uUmSoKWNNgyEYTHodvnv/9l/DVqhAdd1ZxCYJhkkbO9sYNsWvh/QqLfodDq89957jEdjGq0mjUYDKQuklIcuysrcHHkhmTuywua0ILN8KnWfU8dXWTt9nF7exYgkVddnOBzSH434hV/6AtF0gudVUdJGFrDseZw7d469vT1qtTrXdy/RXFDUWx7t9ir97uDAPALfZzoaYxiCQFcRhiATGZNswk5/n35vj/n2Eov1BSSlVkqapliOjes4CKXJDcU0iXEKhWOb2IZPtdIGy2HO8hBaYxxyM3GcgksX3+L118cMu7dwXZPN4S52Du3jDhWjSioLZB4h8wKrJnnn3I9488Vn8fwAp1IrJQHm59lb3yeKYrKRxjRNVo+ucO3mTS5f3uLajT6/8Y8OWB9oCq25+NY5Xnjlba7e3OTy1pg/++b3SeKIl198gSNHjlAU5fcs0cifuv2ZCExVfq5rlxo6ipmQlFYlrKKBQ07iTdtE20Z5ztYaQ8+mnm0DocupWVXOYiGESSXvMhoXLMwvEgpNLg1QILXGMCSWAY620EqgtSjLuRYzo5j3j4pvz9RHZ5KzM919w1AIYfLc5THTQR+TgtE45Mffe4HHr+8R1JuIJMN2wbAVsddm+JlfJq4uoFUpaauVvmMuog+pH7fjAy3ipqGoGwLPsTBqNRqYLDVrTEywZMJES9ypQpHh2OV1Q6cxUpVAhoHANMAzwPccLKMUYbJME60UeaFQhUSqg9W/hGGWxZ5S7EhrylFrUS5ChIPreti2h2dkpApkkaMsjVR6JoBTLkTLsBBGeYFTGshLaVnbdjAP6RvHeWnFNpkMCTyfcDJhc2MDLSyOHF1lv9fj5s2rCBS7uzsUec7RtdNUKhW62/tEk5AkCDG0LN3Xh2Nc12E4LDexPJfs7+/RaB4sLi9+avrVMCykLv+vZRtcu3oVdHniuH1yFBS4ronnuaRpSpqmZFnKeDLEsgyGwwFpmmFbFpZpkKXZrN9x8Bjxzm6X+YUFtLCIopBqkGKbHm6lQqM9x+vvvsdCrc7SkXnyIqdqN0kTyXAYIkjoLKwBBYNhF61zLFswHnY5ttLEM0OiwZg8jBlMD7Zns+zy5pfnOY7lIJD0RvtsjrbYGm6z1d1kiTH9tMdCZZ6V2hKOYxNnGZZdCmYJwyCXBb5hkWQZQgs8rwLCQM40rQ+7LF+7cpPXX3+J+fkqZpqSa01lzqcoNFpLMjGi3mrQaNcQBuS5INobkeUFhdLEwzHxNKKvM4q4RaEL1m9exrBMZJ6jlKC5sMSx02cPzGNhLiCPxly+/A5hMiHOIvKxQps2OC7/6z//5/zPv/d7CCHK/pYQpflGubgwBBjmTDdJ6XJT1Kq8iStFodRMsvXgTd6cTUPevu0opdCinII0RImWFEojtUAJQTTZY1zM44cRc3MVRjIn0eW7alF+npo5hInZ+8wMEjooXEtgmAIMsEyBeWdi08RxbFaWmtRPLHHp0rtMs5TQNPin3/s2zUaTmjBZ8XzaXoVV3+esFeB94T9CWlbp3atV6ailS5z+7xIfbBEXGlcILAPqWrPplyYH9UmFNJ6irYyJVWBT4qdKluPJJSxAqb+NxhQgs7hUIjRKLE4WBXI2DuwcspMWSiJmBUXo8oXFMDAsyoI82zSEKOWKynF2gVSKJMvRSJRpoGYL5/YqUmqGY5lmKRJ0yE5aq9fp7m7i+x5RGJEkCfV6HQwbIQw2NjZQStFZaDMcDlleXGJra4tOp4NpmjTqNW7euM7LL79ApzPPmTNnabcXyfKcrc2t2QiwJI7jA/OwZnCKIcRs/NigKCTdbo9+r0u1VsOyLGzLJkkTgqCCppQaiOP4jggQyLKYj8clDOM4KCkp8mzW/Dl4fQjLZXe/j+dGLK60yFQBskBYBlt7O5iWSVEU9Ps9giDArVbZ2rrOfneXxc4RBsM9PM/HsgTNVo3F5QX2dvawRYEbTNE0qQSr2NWDn0dmhBRGRpFJkjSkO+yy3l8nFCEjNWYv30dkFvFkwjjqo6KEtaWTGCYkKgE5a3IWOcoy0VKjdU6WxRiYKCQYhxvyYgiOnziBlCGGNGm1mgQtH2WUErCmVYHQwCAo0WGZ0qrNkRs5lVYdmUom4wlpIulupqh+ypxXJVMSLWGh06S9ssDZsycPXh+GwbVbG7zxxpv4foXA85gmGYZdSlz81de+xtOf+QxPP/10+T0aJoZl3Wlslq/PDPeVJUwgdelqZRjGTIv7cPgg1aLUZ6fUsEEYaK1mODizxuHMSFlLhMrJTZub3ZCThsNc3WYviUl1+X4K/VMmT7dVFTgUEse1SwgF08SawY+GaeJ5HsPeNvefPUKt1eHFN8+TxCkLQZWnfvU/Jk5SXnrxJd7u9Sh6Xer1Ck++8zb/6Jd/BcsFLcvTt0EpsvV3PIh/wNopSiKKDEPG6CxjPt3CyGwy2SE2Ne3CYOSCa1kYWiClQM+60XckKEX5tA1RbgpCS0xhYNomlmMijFLz+fAQZdND//Sf8kSqdHkd1KK8SuZaEacFtmmTq9tqFj/51m/Ltdx+5rdt1w5rsMZRTJqmGJ5FkiT0+31OnjxFpdZAaUGtVqNa6TANJywvHaFardKYW8SxbVZXV/nxj1/nq3/5F4xGAzY2q1i2SbPVwPcrrK0dIwwnuJ57aEPRMIyS0TJTYQSYTqfcuHEDgFqtSlEUuK6NbZtYtk1RZLN/q2GaJtPpFCklN27eIM9zTNMky3KuXbuGUmp27Tz4exmNx/T6XebmWhw5NgdCkumcTBak3QF5ljGQOeNuzJkzpzGzgjDeJ88zTNNgPBmg8ozNm1tsbO2xuDKHVBmNVh3DbTAc7VMQsrR874F57Ke3mCYZVXOed66fZ72/j79Yx2pUmW7tYFddEqaQpBRqii01q51VhAGZTEhyhZaaQmYklIVlp3uNbu8WnuPMNEfMQ6vFvfc9wNWrEpjw5rOvMhiFnA6OsdzpUHXqpELRagaYwiBLS/hmON5AeQXD4QDXdGg0GzheQCG3uHz9JsUU5trz5GlCkUXsbN2kPxrwa0//6vvmUWQ5vW6f9fVtfuM3fpPvfe9ZvvSnf4ZvuQgNg+GAv/jzP+epp54iCAJuY7y32SpAiYuL8kAm9Ux/5rbukGWhBYeuD6lVecgQYnbtvU01uA1NaQw0wgChC5qBzW5usycz1P6As3adxSBgN86QhoHAQKJmmkVlTeH2oe2AsC2BbRpglJuJ69skUciz3/gKL3/3m5w+vkh9TlCrNxFM0UXCwvJRGvUqmfYYjGM+9vQncH2f9Xffo1K1MUwLrUDNoEqFwSF6cXfiAy3ihpZY5JhGzrhu4D3zDubVW3if/gTR2jJLAxtLxqQCPNdDa+OOPOXfFpMqRWzM2enREALLsshlgSnET7rT7xti1h0XGLcF2w2BYfwEWlD8pDgrrcilRGkHYZYbheKn9YH132LcF0VBkeczJbv3j8lkTBzHdBZWuHblKnNzc0hZEEUxC51Fsiyj1ZzDczwqlUopZiU0165dw7Is/uqv/rI0cq5Xy7+//h6maXLs2Ck6C4sIUYr/R9HBmPjiYvlZ6naDVmt6vV7p/tJoYlkmWksqlYA0y2bXWuOO0Jdt21iWhZSS7e1t2u02WVYwmUy4fPnyTxrIxiGbWhJTFBlpFnHp3XdQKidwatSDFrWghak0N27d4vrNawwGA+5/6DT9cMBcq8N0GmIYNm++cYnzr16k1VnF9QyaCz6thTmKIsf2UuI8YjDcYmnhrvf/XtI+01hielXe2nyH2pFl6scX6YZ7rPe2abcqCFMTJRGjyQirWiVSCZ7wMAvNcNyj3+3SDGrY1SqDYsC7m+cZJ12EUYC24KcL3PvE6rG72Nvb4datLgtHltjt7TCIB5h7YFZtUmuCLDQSE619TO2hk/IGm4YpmUzJfVg9ucjd95wgD2MuvfouEJNEQyzRJKhVsPyDG72WMFiY7/DJT3+Gj3/8E2xubJU3VF2aW4sg4G+eeYbvfPvbfP4LX0DmcnaqMUqcQ5RON6V2WPl7W7P3SFAeIgxtHHrY8E2BLAFxZvbms8Zx+TEWgJgd8LTCUzkNz2JDF3RzwZX9KWeXXBY9l67MmZFqyv1AlESBv8s6NQ2FYZSwm21p3nz5B3z9y3/I5YtvsbZ6DK3mMQwDPwiYTqbkSvLm+UssLCwwnUwQsqDiCkxDkYUjdi9fxPZ9cilLdpjlEEcR+3v7fPKxg6Eu+ICLuO+Y2JbAKDL82KP5a/8JvT/+ffiX/wNnf+kfsvnQ3wMPKlJgkKNRFEKhFQSORV4UxFmGEgLHs0r4QxU4WqNUTpGX2JphHvxrKSVnXW7uKNCVhaYswLIwEIXElODZFnW/ihc4NCoO46hKomyiPL1j3lyevBWSGdNFGDNs9BC7KaVwXY+dnX2OnTiJbVr0ej1Mp2Chs8hgOAChadTqBJUaSar44z/+t8hCUqvXuOfes6xOV0jiiKc+9gTHTpwgnCTMtTrYjkclCNjcWOfuu+85MI3PfvazOI6D1pr33nuPr3/jG2xtbTE/Pz+T3jUJgiqGCVkWY9s2SZLcuQqXeLk7w73B8zzOnLmL69evl7rSrvu3T2XvE0mSoLVmMBhQr6yw2J5neblKFkeM+zdo1U/THwz53Gc/x30PnKQ32OTdq1tUfM1kEjIc9dFmwdqZIzTmjlCIEf0owhkGJGlBs76A5UE/Oli1bxwO8Ox5+pMBW3KPX3jiEwzGY77z/W8xGXbxqycxckmW59za3SSpxNQDAxUbXLl2i3OXLxElMc1qnYWFNpEKubp5lX6coKRJrssDyWHP48qldVY6d9FpLfP6688z7/oIM2ccJ7z79o+ouj6/8LlPE4Yhe4MBY2tC01BEvQjDdEBazNeWaOl5tne3aK/M8bl//HO88J0fYRcGjXYFt9EkiQ9ep8Nej7Vja/zGb/82Ks+wbQffLx2BXMfmi7/1RUxd8Ndf+VOef/Y5/tnv/A7NRhOhQecFhdZgl+SDSTzlX/yrf8kwnPDrv/5PWFtdJU8zlCjphgfFou+gpAn8pClc9rUUzA5ktlZIDMx4E60TFp2Ym44gShR704T+jW3WWlXm52wSbSMwyqa9Bgml4uAhzj41kaHSlD//k3/Pt77xFdZWjmI5DotLRxiNJrz55iV6vRDP9/F8C2FXKETBNBqwuNyk25vw2gsvl85ARoUv/emXsEybOEpwPZ/pdIprlZZ4X/wvDug4z+IDLeIaNVvAAp2kaFGjcuYBxs98G/njl6g89lmULMX3y51bs3T0KJbl8PWvfYPefo9jp0+wvb/LkaNHOLK4SCvw0YWEXIPl4moDfYiPoVAaZNlJVsbsKoUmxyDLc1pzDZY7TaoLq1imTS5zMrdOWqR4R1Nsw2Q8GbHb3WXr6hUUerbllL6IhZDkeXqot2WWRAhhlEwC1yIcjenu97nv4Yeo1WrU6nUKJbl24zo3b21x5fJ1er192u15gsBnNBnh+x5zjTqNeovlpaP07CECge04uJ5Hu9Uizw5u9N68foX5+ZKGeOHt8wwGgzvazaZplidtu4RMLNMCzR2jaa01cRxjzpyNptMIjaTRqNGaa7K/v1+euAzj0BNOu1lnPAYhFHXXpyIsfOHgBybpWNLqeNx933H2unus9JepBitE44s4ZsBkmGIbNba6m6xvbLLmOUg9xtc2eSFBmEymU7SGfnhwY1MXGkc4ZMDW5jrReMx0NGZrfRMLiWu7oHImWUxaFBTTCTvRJlGsMFY83LDG3vqITI5IJpK93V1GUYwyXQxl4nuzU+QhRXxraxNkznjQx7XaLLZqxNEeWmScuvc+Lp57jVdfeZ3HHnqI4x2X7WRAIlPcukcRKZRhEmUx6xs3SdKEokgQlYBHHn+Qa69cIhAFw/0dTp167MA85ubbSCCXOb7vc+zkCQLf4fjqEj/zkQ+ztrKEZ2rWVpbYG055581zPPrhx2Zooy7ldJWmUIrJeMK1q9d5+92LVKs1vvibv4lFCY+oQ+wMS6ihuHPouvP81MyoWAiKGSc80BL8KuNJH9uoYsxu2lrDznjKcnuescpLVooGrUVZCxAz7vb7R9URXHrvEpfffZtTJ04wGk0oJhMs16HZbDLY77Gb3iBMMuYWWvT396i9c55Tlk3bdxFuQBpUkbaPrCQ4p84SGQb9vKBeb7CxN0EUMeu3bhyYx+34YCmGWiBnDJIChQgnLJ8+w7Y/T+/COu7eLZL2Ap5VGg03GnXuuf/+coBEp2zdWictcpK4zxsv/xDjngfoewErD99DLTPLAi6sQ7vL0zhCSBDCRlKUHHABWZbhOoLFY0+ymxjcGmg2u10GozGnmzbH7rqL00eXmOzdYmfrKmIywNYZo0yg3LJYZHmOck20sFDi4JNFXhRUq1Ucx2Ew7JFnGa5bDuoMK0OKomAwHDAcjHj33ffY2d5jYaGBlJL19XUs2wKtcCyB41j0+316vQG25WI7HkkSo1EMh/0D8xgNehRZwmQyYWtrk2azSRiGd/jjQpigDWRROt0nSUK9WidJErIsI8/yUkNZGCRJTK+3T6NRA37SzygbxYeoPBQZnbkmjuPQqtcwkAiqpEWOWdHc3L5J58g8Vy7e5Gtf/RaL80ewcfBMjzjLePO1i/SHA2oLFaSTUa9WMRHsbO3juj6VSpVwOqF7yPNwhUvNrZNGKTs7u7z19ttgu+zs9yiymM+3F0EVdPdHxElOOFFcuPIec/PL3H32FMsnTvLm+fOce+McicqxbRfTzMllgdaKyoy/bx7SgD+6dpydjVs05ubwXJ8sm9Juz9Pd3SKJx8wt19jobZG+UfDxJz7GyYrHq7v7+J5DoE1Ms85gOCbwHQbDHQwkNyYjFteWWT17CjtKaWiTvWtXDv5aZu5FCEGSZKytHePIUofHHribx+87zY/eeJMPP/owFc+lkafk4y7Pf+cZLl+7Sm88pN1s8/GP/izdXg+/UsN3PLI845vf+ianT53i5z/1c+WQ0CFwiqbsV2lKckIJg4tZB7DkixVC4esQncZMI8lkOsZIFY5ZQ5X2K+RSY2oL08zJtKb0WQMtZvzEQ5bp+pXX6fX7bO11qXk2tu8TVCqkYcSgt1Xi2YnGQSG7Pbw45b1LF1kXmrVKlYcaTU425/B8H3HqQxTGPLoaEBomV/KcsSkwhEfr1PLBicziAy3iuVKkWYaQkqnKSKaaTrvN/skzGK0Jq02fEw88gl9xmGs28H0fDIVtKT7y2P3I+0+T5JKP/cyHee3HP+ZbX/kPbFy8yBPrT3L3PXfTWbmLTFSQ1sETaINohCkzhLZQunyxlNZ06nVO3HU3z+1lrO+N2N7v0u/uo+IxT3z2SR6eD3jt+W/xtWf+gu29LcgzPvrJz+EFVcIsKZkUCEy7QmCauObB11TLsphMJhw9epT9bkExcxaKk5goitjZ2cF0XC5fucbWzg6dxQXarTq7u7szhxPY39+nWlkhiiYI00UA9Xodx3UIp2PS6RTHPTgPIQz293uMJ2OklARBQBwndyAQ0ywHoRyn5EMHQUCWxCRxTDSdlgMcWpPl+ez/xiVDRSmKIsO0DGQhSobCAVGp13AdF9u28GpVLFOTqqI0CMAkzwAAIABJREFUFUgzisJCuB6tRoftmwN++MMfcebue3njtSucP/8maZbRmKtTW2zQaq9gGRJRFIAmScrfRxaSZlA/MI9smlFbrBO7EZbr8v3nn+fIiVPESjNNEnp7A5YW2qRxznAYsU5MlvUwRZ05v0V7cZH9zR2u+gFZklKrBEzTCGkAs1IileSnLSf+30IqTa3ZIk9ihAEYJo5tUG+Uwzp5tkUyHXJjY5Pdr36VT37kMWxTEMcRUQQmpTHJZNKj4ltkUU4SF6xf22bt5ApvnnuLs/c+iDiEn53kGcaMNihMaDQb3HvvPbTbbS68c4GdnW2++4MBo+4+880q99/3AKsn7iIadfnxiy9QrzXZvn6ThcVluqOIaqNOEFTY2d7iy1/6Ep/5xKdA6TuDQO8XGkD8bUoscKd5JVFIFK6OSeOQ0WQCwqWq+ti2z1TZJaMFwTTOCCoWWpWmDSWp4e9Uw3nhjQuoUYSlwBMCI0lIoylZFOPJlNiu4tgmc50O/e1dCjSZUAxR7AyHvDoZsrS9xYNzTX6xsYA9GZLYFaq1Ko9UfLKgynrmovODWVS34wMt4lmhKFRJ0Dc9Bz/wuXTzFlc7HYpWg/tbcyzU6rRXF7Esg0JKuvs7rF+/ikgiimjC8Xvup9Vq0Wo1eepDD5FPJlh//QOKc6+gf+5p3OOPMPBaB+ZxrNNBCQOhDVSRo4RG+BWai3exJ6q8vTtl58pVVDjCTMecmvP41Md/hpe//Qxf/us/YyAkYnEJkefsTyZ0OjUC00TmksK2aTgmNbeGdUi3/faVcDAYlMXT97kVhizMxt7D6ZTNKzfY2dsnqFaIkinDgWIyCUmSGNfz0LogTROE0IzGI/Z297l1a5PVY8cYjXoIJXHsg79mxw/QhokXVLhw6TLCLAgCv6RvCYGU6m/ZT5mGyTTq01nosK8lSZpgGiZZlpWbtBCkaYoQmqNHVpBFgdZlI+ig0AJyJRHaYFqkyDRDTDVFmhGFIZ2Fezh96kEeurfCYw8/yde++lfs709Aj5lMIlaOzHH05DL1pSZRGmMKhWuUdDLbNhgMBuXvcMiJr9ubsM0ejUaTwHO5ub9Hq73EkflFrsUhP3j5dVYXO+z2BsRSsTcOsVTBKJqSGjluzeWBRx8kUinPf/c5QpVRzIq3aZSuLdPplDg+uOGcZHn5vlg2Ozu3qAQlW6neMNjaHODoKrZIKUxIVcGLb5/j8z//MW7sbnNjvIecTnDtsh9RyHL78JwqSZazu7PL3iSCmxscWQsOzCMrMmzTwhIGpihJBvVWm93BlLfffINGs8a1G9fRWYKZNdhtVWnNNVlq1bjv+BpxlnP52iWiIuWZZ1/io08+WR7QNHR7PWzHIc0zisPoGIZA6LLEmtyGU0TJT9EaLSSeBj+NUWZBULWZM+p4lTFF0ODyfkyuDFShGYYxy24Vaf4ECzeEURIaDlkf490hOxt7NFwbHU9JC0mW5SRZyb3XpmIUx3hZTqZycikxKz6iSGk5mrSAnir4m71ddl78Pp9YXePM2mnk/hYEDp9qNfix0+JC/WDv09vxwbJTDIFp2iiZ09/bwZJtztz/MB/6yBO4OPzrf/9lXv0//x3jaYRj28wvLHB87Tgn1o5ShBOagYu/klGEPZ579Txz7SVO/9ZvM734Fte++ufkL77MojNH/a6Di/h/+5/95ygtsTTEUrGxN+S5t27y9vqU3ekAJl3OmAXH7jvBw/ef5dF7jvMvfv93efaHP6C+skZNCCwPcALWN3YZ9lIevedunvr441QqLpYOkYDID368g36XaqXKsN/j6LFV8qzg9N338siHHmE0GvHSCy+TKYkhBNPxlCJPCb0Sd6u1amytb9Gq+xw9usRkktCeb/DIww/R6/ZAZVR8l73dXToLBy+GJ558irwoGA6HfPt7P2Bzc5NGo0EQBKRJQqXqzRzgS42UJEloODZHFzt88qknefYHP2BrZ5ejR4+wu7ePaZo4tsMTj3+Yhx94kD/6oz+i01kgzQ6zq9PYFQdh2Fx46y3W1haoBBU81yOw55k3W1x7/SKGUXLodRoTiwxfFPzqL/0C1BU9NSDOQ4Y3JjiOP5MksGk06kRRilQFjn0w9np92Of4qkUYjTAw+OJvfZG33nqHKxfeZG11hfWdPWShkLqgKDKiaJ97T53g8//wVzh59yls02ClvsRDPMB3vvlNRtO0HFgTBnGmiJMRge9SqR9sYF0NLDa6t1C5wiLn2uU3qT38KAtLS9zcsqlUW8zPNxh1R4wGMe2FKm9duMJ8Z44nHn6IpYUVRqMBF995l1vXhggro+abNL0K+TRh7uQZjp59gEp14cA88twraXyGxlWauuuzt7PNc889i2W55DdukScRa8t1hpHm+q0b/Pj8e0RpRpzk9McTtna7fKLSQBuCbz3zTOktiabb69Edj1DCQSnJiQPyKK3D1R1i4R2YDoEJmLpgwZhSdwtSbdGyAnx3hXoUcuJkkyfuOs5r13e5sLlHPzaphDGNOZ9Ml8J61gwfNw6xM7x2/QLb3T6WsJEYVIMKbt3EUprRJGayu4ltwvrlMbYumTO+NnHdKpM84gsfW+LLz24AcDmN2LpxhX/69CeI7zrN/vYWKs9oOA2SW+MD87gdH2xjsyiIxhGomJrvU2QJm5vrOK6NaVs8+vgTJNLkh889S5RI1jd22dre49y588gs5/jqGq9eXkcZAq0Uk0nEdBDSsj1O/YNfx63NYQYV8kN2dMMTGIVJf5BwfXsXbbosHT1OcyEvxa0Mm8IsGxChmXNuM+HIRz7Lrz3+C3hmgGnkYEmkYfH25aukk5DVu+4Fr0kuQDg+Nd8gOKSR57kurutg26UAlet6rB07jixgZ2efMBxhOyZpkWEJqLo22oZ4OqTdbnPyxDKuY+B5Lq3WAp7rYgioVPwZ59un2WhgH3JN3drexrNtsjji2Ilj3Ly1wWQSkssSA5eyhA2jKKMoFELA0vISwtAkScTScofBuI/rmMzPaJL1RoWV5Q62bYKWGIbAPQTW2dnqUmkrKtUG1WqbNLNYWVrClA7D/YwbmzeYTMpBotFoRBiGHFk9SmC4jIYTOvMd2lWPm+vX2d3dxbJsqpUqjmuS5zm9fo8g8KnYB8Nt13Y2+Oj9ORU3IM8Ulm0xHPZASI4fX+P6tReQacpip83R5UV2d7scPbKMEAaB7yOExLQt2vMtEAqrVIEC08RxfZbaTYbDHlrLA/OwBDRrVWSquLJ5lb2tW6w3G2RZTOBakEtGoz5pnuMHDlEY0Wq1mU5jJpObhIOYI2urrJ2+i+u759HKYpxCmhdUW1VqjQ4nztxHrX6wts7mrSsEvo3vGvSVQivJ4mIbz3cxDZdub0Cn3cTz6nQHQ/Z6m5huk95gxF63j2VaNOrzDPshZ+85yyuvvEKeJNiOM5sxiDANzTScwt3H3/95GGKmkVQKVv0EURGzEXyBrzOKyT796ZRWvYVTsQk8H5WMmKu3uWd5njDJuLwfMkoL5pQmMASFMMln9FrjkPf23Ss3yXKFbZmEUYRru0itcSwT07IoZA6mB6ZFLnMMBLbrgCWo5QbVXoX/7thJ9grJH21cB8NgPBwSbu+h4gxhWQjLxj3EC/bOc/k7/dT/R5EXJpMwo8gSmg2X9c1NXntji87iAidOnuS1Ny4TTiMeffhhXNclDENuXb+GLgp8x2O+3qTTWURYZWPIci28ikfN86mYNqbQZEW5qx4Uhi6IhwkvvnaD12/tlIIztocjSmGdRaeK49cJlkxaHRtHeKjjJ8jjgiKNicOEcBQihYGlNG4lYBhOuHQlRlDQnD9KoCfctXbwyxHHJfYdJwkLUiKLgsXFFXrdMT96/iUazYDAd2jUq9x91wnCyZA3z18kiWNMWePsfac5cfI4Qtgk04Rer8vOzg7T6ZTTp0+zurpaimodIsR17epVqo7Nwvw8J06dYmH5KNtb20STkKIoaFSrTMIJSmpMwy5lBVybMJqysbWOaQva7RbCcrCFQyETXFuBzhkNBqW0gZSHDrd0FltM8oy9/U2WF5aJkynd3pBABKjEZGNzgzzL7rBiXNfFsgWOaROGEePLNxhZEUkWYlkmlarHbbZpr9eb0SghO7h2kmvFJJmyUK9hYdPd2yOLpzhWeRoUArJcYlkmp0+scezIUVaWFpmfn8eySplVhCDPMmQhZ4Mtkorrc+LkSbSUhOGQJD2YNbS6vEA82iVWGc2Kw8mjS+h0yvbNq/iex3B3gzgO8f0qzXqDfrdLGKYgckxTcGt9i8s31rGrVT7y6ScosoTtaxvs3NxATaf8vcf+PllW4PkH3wiee/bryLxUMtRa0VlawPIFneUOg14EWhBFKbvdMbLImSYRmYpnipoufrVKrVLh0ruXePCjH0apUt7CM0tD4+999zscO3qinLd48kPvm4dQ+o46IkLdEdYSM754YOT4RUQmE2qBi2cZaMum7tWJ8hG+W1DxBCeX21zvjolyhSwUFde8M62nmQl4HRBSa2zLLGdHBPTHw3KCVGk0iqYfEPg+eVGgjPLnL/d6zFdroCT/7q2LfP74ae5/8kMc+e6Efm9IHoYEnkeWxCWsk0Vkk+7BicziAy3iUlkIw8MPTKbTkFq9jecHYIBpV/Ach057oeRxa8383Bxz1SpZnBA4Lu3WHI7tYDo2WikMNE6qsA0wXBtlZpiORZIfXLTssODtly4QRiZO0ETICMuxKPDIXI39h3/CvHaYe/RB7vtPP821Bbh48SbXrt5iOp0g81I2VhuaOM9QUnH98kWEllhewMcfNzh3+QJCHUzdMgyDMAxptpqkaUoUJbzyyit8/7vPMxj0+dzf/wxpnqJkTm+wSxT2ybOYZrPO8eNHCQKPLCuwLZ9r168wmYQURU6r1UJrTb/fZzKZoOTBqzLPc5TjcfTEaU49+AiGBoOC3u4GVy+9hSwKTMsmTCTDMObi5WuEYUitViOOMmzbptGYJ0lzzEAwnUqajTpo2NzcREpJkefoQ3jAwsypuBbNVpUkDlk6ukQrqKGnsLG9W1InbZuiKDBMkzRLObm8yOnVMzz/N9+hNxqyHm5TbVSo1V3m3Dq27ZBkCcPRCEMIPM/DcJwD85iGE9I8JUkz6l6d4d4+ngXL822OdDrYdjldV+QZVd8BzyScjEspAM/HFApFSVsTwsAwSxnV1eUFHjxzjHMXLuEHPuPJwUJcJ9bmaVXvZfPWFka6S2DldPsjCqWZJhOyOKXXHeH6kkZjibnWItMwxLQ0eZ4Q1BosLh+h0mjRqPlsbW6zsFDFER3cisGgt4E0PB6474ED8wiTPaaTCb7jMBz1aC56dJbnGIUj9ntjPvLRhzEE9Ac9LMtgwbToj8acrK1w//334dkWb75+juFUcfm9d8vveqY7blsWf/mXX+XRRx+hXq8B/+B98zCFAFFSVU1rxhecVVyhoalGeKrACuqYusAxNXgeNbfGcNwrNX9skzlXcKRVY2swJckUVd/FNcCaHe0P46tPomlJnDTMcorbLHAdm0JKPNciSzLCPEcVOa5tkmWSbpIyTDPWAoeKX+GNNMReXeJEa55wMCZPUph5KyAM4nTMD/7yj+EPfu/AXOCDLuJIbNcppWZym0JqlLaIk5S33rmMUJDMxrdvqwDPdzqYhkDIchfWRqkYhjCxBLjCwFQSU6YkeYpfq8/w2/ePr33l60x7Ba0PP86NN16h4iiINU7zOGtrx3GHOyz2Y6xwxMvdS7T+ya/Q9CwansnezgTQqCKjkBlFkVIUEqEVWhUI0+Qbf/S/k2Qpc1Ub+OX3zUNrTbvdZm5ujudffIFHP/Rh3n7rIlvbtzh9+hR5Bm4wRxhNuHJjE61jpnGKFlMmYYTl+oxHexw9UmF5eZFTp07hui6O4+C6Lrdu3Sr5seLwa5lfa+FVm9QCD5mM6W3eYOOdV9m98hZ5rqnUWjQ6y9zz6CMoIbj89muAoFqtIaXGMBwMQxInMa7r4Ng+uzv7jEcTtNakWYZxiByCF7QQhsmNG7c4cfwUuhCsb+/SCuZxq3WEPWQynjKejEs2gio4c+w4rVYT6QqiyRSkJPACkmjKxs0t6q0GyjDIVUE4GNFsNikOERZyDAMlC7K8wDNdNq7dpLPYYM6x8S2LpU6Hze390txASYbDIWfP3ofrOhiWhWfbpd685WGZLlpo2s0qjz98P8eX2+z02kgK9vYOLhbj0RZFNsFzCxoVF1MFxNGU9c1tiqIgHI0YjzPyQQ/NTZbm2ti2otfrE9Q8qpaNY/sszi1y+fw5TGGyuLjKfY89wDgf8d7Ntzl514Osdg7umYzSfQzbQFowDAeE8YDO/ApBxWc63ebe+9dYXVvm5q11lJZ0Vkrxp3azQbvlMQ1D7n9wlQcfPcOffOnbCNNBa4XnlYJqg2GfUdIjN6YH5pFlYdk0jxPC6WDWdAehNUkUs3aqhlAhUudYWuNaJmGe4VU8KoaDkeX4pkPFMlibbzGYpKSZJM8ieoNdHMehkDm24wJn3jePn334PgbDMbvDCblSRHGCMC3IC6q+Tz/X5K6HGXhEScwwS/ECjzTP6eUay8qwbJt2e5Hn+vssLyyQpwm1ms/RuRbHFua49f0XyCejA5/H7fhg4RSZME3GoFPiMCPPY0yzdNhQGOVOK2VJazNMBJDKHKTGxMC17NKVI88wTZNcCFKhqJga7BwndyDNscyDT1obwzFrdz1I++Qqnb1t6q4mjELmj7a4b6nBjcfvhWnGKCzoGppHT5/id//L32Lp6GkMt06UZSAzoBTSMgB0AarA1il7V98Cy+TC688fmEcYhiwuLrHf7XLvffcTxQnzC/N8+ulPYts2cVKQ6IzecIpfW0AWEYsrPmtrx4jiiDjRZHGCzLeot1xcr0JRlIuyyCUrK0cAOH/u/IF5mJaF7TgoWTDu3uDGO+e48PqLxMMd4tEeluUy3ltn89o79Lt7LB45w7rnk+cZWR5TCQLyQiFMh0JFIEqN7TRNcRwHwzDIsuyOZvn7hZQWFa/CfHuBVqtFf9BnGkVk0z0mOzG9fp9eb584TqjWaji2hWVYfPe732Ov3ytfvolmPBiBKKhUPGSmwATfcZG2jaUE6eTghtHJlSWqno2UOY4yefutt7DcuzlzfJVatc5TH3uC5557AWY3xrvvvYdPPvVJOvMd5IzvbGDiuT6GMKkGDh965GHO3n0K4jGtRpVuv3dHuuH9QkvJK6++zNLiMtguu70heS6JxhOKPGO32yNOC1y3glYmW/sDUBO0zmgttMkzTRJNufj265hCcM+9D9JsNOgPu1x87wKWZXHt/CX2H7sOa+8vQ7B0pEnVC3BMi+WjdRpNF6+S8/CHz3DhwlUwp0TpPvOLNmEUMxrcgGxIMk1RNY1BwXC8z4lTp2hVfYbDhKDiYXkmWa6YjiPCcIxfPXiT//Kf/i94nkMcR+xsr5Nmiiw3sYUinE755X/+u8TFPiqLkNTJ8pQb3fe495HTeI6HTKZYfhVhO9R8zXy9ynDU4/Xv/gfeu3kBx7ZJ8wjTcfivfukT75vHqVNNbGueyzf3uXi1i2lO78hWWLaDIaZU6hWkViAMslFIYRhYbsAkjViSimaWcmxjm5+dW6RpeZhxwinPZ7y1y2vPPIPXbmHWKgc+j9vxgRZxV0hUkaC0wUTmuIbGpCBwHTIlZmYEgkwVd+hsVqaxDbPkyRYlV/m2ZocjzFKdJFfkZikDWWiF4GCscfnUafx7z5BPMx599EOYlsB0POquS+Bq/vG/+X1G0uCHr1/iUyst/u8/+FeMd65y6+3XWbnvcdz5NkWRYCgTJUGKYvZCS3Qy5uTjT5OO9+ntHDzeferMPdi2TRjt0ApqXL58mfF4jOvZM82TiPFojCEEk36fcBIyDsfs9coTSbvRZGVxCaUz6vUVbNdHFRLPdqlX67x7+T1s22Fx/mC2jm07JNGE4eYVnv3z/41JfxPbctCq7PsXeYZNimlA98pr5FlMENRYWp5DiBzbKYlZQdDBdtoYhsnuTo8bN3bw3Aqe788kfw+GuV5+8Y2ZjVrAWxf2yAtJrdFgvtVi9+oOb50/jzBKW68sy/jCF77A//g7/xO5NDh6fJWg7pJFCtcSjEZDpo7F3noPIeDuu1c42myycbNPa+Fg84G7VzWD7iuoyhInV2sM1TyrRxY5duooS40Gn/rMJ/n80z9Df79Hs9lk9cwZFpdOMZgMUHkOpo1WkiJPadUDHCE4tXqEzsIC/Y2Eml8lzXOGYXhgHv/HH/xbTp9e5b777+fZZ58Dz6DITRaOnkApk4/+/L2E4RDHs/j4Ux/DUIKXX3qRUX8XxxQMB0MuXngH24XAr3Pjxl+UWjeOS61ao1pv8omf+zgri2sH5vGLj/4itUoVz3GZrzUxLYHleyyb53n2L97lv/nN38G2DfIiQcqMVMaMohFxHM3E0CzyIiNJR3z0Zz7ES89f4g//zV8z7IZ41YAkjRjuDXj4gfffSADOvfZDZFGqJE6jEMd28A3NOE+55+6zEG+RZC6DxGGSTXHyIYVp4np3YeDSH3fxnQqB7eOJgpOdKu9EfTb299i6+iau7RG0Wgjn4M3k//rq62RZAlpz+vRd5PnWTEpXs769Q6vZZrc7JE1jRFGQC82nPvZhnnvxNZTlkHQWCR9+mO8XGfVf/nmiSsDykSOsPvooaZaiT5/kpRd/hGH8/9AUwjRMPMdCS6h4NuQSlSvSJEUJgZErpCxK9slsws8yTaRUM/VCZh6UBaZpUihZCuJIRSoljhVgCKN0+zggNnc2GRoutu3T9ANsx8B2Laamg2WASGp0w5wbGzv4jsXzL7/C6gNPIs+9RDrq4zZbiLJjgZIFShR3ONXIjMwKMIMa1iFk/WmUIoycKEnxkhjXdWdTljmaAiEUgWvSqFZZaNXI0oy9fh/TMmk0mhxfPYbvelSrVcbTkMWlRebn2rx38RI7OzucOHmC4XCI5hAWhG2CypBSsbB8kuH2dWxh3mmIupYDWoIJSS6pZBIpBIgSNlFFzqC/j9kesTcZE1SqmMKiUmtwZPU43X6XOJoeCnMVeY5QJnEYIxnjBRUqlsON966xs7WDmqnXZUXBaDzmT7/yZ6ViXVow2N1G6xp5ltPd63Pm5CL9yZg4y6lXPVzXoFHzGFUs3ENuBI4v0TIh09t47SoP1Y+x2KzSQJNNJ6SjASQRK+1SH8QKp5ieiZnZGLpsZBVK0mw28F2Luu0yX61hFgqRSza2d+l2h8hDehWtVpNJGHLu3Jucf/s89XoTv1ZlabXD009/jr3BiO3tdR586F4+/OiHSCcR71y5SRhGbN+6Rr/fYxL2ac/XSGPIZYZjO1T9Cu2FRY4cP8bpu8/QHw4PzOPrL/wVy4tLeLbD4twCzWYNv1Ll5uAGUyYUeYpjewR+gGFUMGmzVF+dKYOWdBKlC5RKkCr7f9q7kx9J0vO+49833jfWzIhcas2q7prem909PWuTQ8+IouSxDViAwYsBw0faguA/R9ZFF8MXG9I/IEGwAEuGaVA0hxyzR7N0T0+v1bVXrpEZ6xsRPkT16KRsHgSCBcTnXIc3szKffCPieZ8f71/9EIc1/ut//zPSSiOkIFlE+PbyB6yqNOm0faQyycIUT5koKZinmg9uX2e6yDhaKB4eLChVyUBqfv7FL/jRDz9AKoGhSooiwRA2QhjYsqDb9qiETRYnGKXg9nfe4uR0+ebLVBJD1OEoz58/PxtBXbc7lgL8dgvHcSjLgulwSDwPGaxvsbbS5+T4lPWuz3qnzdUr11lZWSHXGR3H5E//5D9z+85bXL5ylTjJiRbLW3K/fV9+rb/6JyIMgRIVoBE6xaDCMBVZllGVYJR1wcirgrKqMJUJZ/PETdNEGvUBIK3rUGJlKjDqU3hSmdhmUvcov+bB1cH+CDc2kZbNYbuNpG4Psk0TU0kORm1G45gKyaNH++y+GPHm1e/S7q2wiFMsr4NhWsSLEKkTTKP+wZFKoacnVDpjZbXPMF1+2T6aTNnY3KTtd3jw1Vd0uz1AEEUxFy9uE8cxmSG59MYOSZLW9xFdmzhJuHLlEllaH9vv9XqUwGg8rkdj2nUL3cFBfe/UafvL/y9nDwyzosQL1lBOQFYVIAVSVaSZAZWNaVpYXgth+qRxSThPSRUEvotrO5AmtOwWtu3zbPeA0WzOlRseV69d59HDr+rTcUt0130s02K4t8/NWwO2r7yBrdq05ILR4Yx3771JVVX1/7eC+5/dp9dbIZokVGXBzsUt3tu+wIsXu3z8g7f57OGXHA2HXNxeY+fiBtur62wPdlD28ql9lRBIE5RR4PQttt0VAsvF0DlRnhJPhthVRds0EEVFOR6jkxjXsSi1Ud9SqQwc16bje7yxukHHsXEwWO31+fyLLzk5PsUxl39O7927x+0717l//z6dzipt32dlfZOrN25y76P3ePr1Y95+8xrKNPFsB1VUWK0eQWedl8kj5mGELkrSVLPI57T8gI3BNoPBBrPZhO//4CNWBxu028tPsO5O9kiMhH6nSywX2KnCtRzCNMZQKV/v/hIlFVJKpFQ4ysM1fYQhsC0L26pH1tZhKQ7tluKP/uO/ZZ6N+S//7c9p2T2UEOSvmbZp3/qYVOcUZYHtXcNUUImCdQOuXL1LlCrC6Zh20GORlwgjZWPQRTktdLQgSxfMkxNaXZ/ibPBo4HlsDnYob75JXpRcuvQd0vnydbwKdhfCIMtieDX98Gwy6vPnz/E8D99vk6QpylJE84i3vnODvxlOmccLJuMRx/u7HL/4mjRJuPPOewTBCmWW8Cd//Mf85Kc/Iy+Wr+OV32wRBywJpdaYFOhCn+0R68Rq91Vgb1VHMJVliTiLQVJKkaYZeZ5RVRWO41JWFWmaE0cptmVAOceyTIJg+cuaz0NG0zlZlmB0e1hCICuwTZONwRaj2Zz5PCZotdh98JIiLxllzi7dAAAOjUlEQVSPp7SCDkl+Umcq6hxDaESVoKOMPMvQWpNOTlkdXGQ2GX6bIfiPsWwHr9Wi11/BPwlYW1sjTdO6Y6WzhmHMsKTJ0emYra0toiii2wnw/TbSgHarheO4RFFEp9shnM9Jk/o+9Pb2NkIa6EKTpct/0ZVSKNNi//CIzcE1br37rwjnY7SOWcRTstig7bZo+S1avQ0mMQSk2K5DEo0YThaYZU6aFjjdAaNJzP7RmAKB57ZYW1lnz3vJ4jWDp6JkRjya4wrFhZU3WIxzMrWg7Xv87r/8kHA0YnNzgJQWhjC5dOMSTx48Q26avNh7wZ07b/L7//qfM5tNEdmc1orL8/1n+C2PTttn+8I2t275VPbyPvGiUPUkTKGwTUnbtfCkSSUMhCERWuM5DrIsKPM6OjA5OMJZWyEXFcqQaJ2TRxGb/S63b17DUZIkDGm328znIaYwcLzl9zz39va4fHmb9bV1PvxnP+Dm7duMZlN0WfDg4efsDC5gmzYnJyPm4YJFFOJ6Lar+CkKaLOL6qu7g4JSPf/9jLl+5ga40L/dfgKhoBwHjyZBOsPxHzTMdpqcTZA5ey8MWAnRJz3H43u3bRPoUnZRnaUgFhlTo8lV8IWcjow2UtChKQdtyaCmPD75/m7/+X+scHxREccre/uHSdZza6wiz7gWUgcBse4xPR3Qtm795khAPLNaDdeJZhBAmWKsMNs4SqRKT0fCY49lzVDfH8a9iSUHLtVnZusqTySmWpfhsXLKbL9/0GIasmxlEvfl5lc/7KuC4KivCeUg4DzGlxLAU3zx7xGq3h2EIpouIMIoYjU8JR6cYUjL92S84HU6gvMp8/g3K1Bjp6wYA1H6zRVyA69oIWWE6NuPplLwosZSL0CWyhEJrLCXrmCJAmSZC1oMnTWlgKqcu4rbNIonJ8wyda/IsqocEac1kunwHHMVDHn/9kDgc43bXMKWiygvSNGF9MEDYPrqqcGzF7HgfmCNFneEXz6ccffMZeZpQFQlCx9/uMqqiJByN2VhbJdMV8jUjcT3XYj4dEU7HbK6vs9rvkyYJszDE8Tz6q6tYpkEax9/O/P7i/v8jjSOEYbGytoEwFLPZjFIYdIIu21tbHB8ek6Yp62srvHy5y+hk+eWh7/uUuiAvBfPcob16F6eXEsVT3HyB5zl0fR8q2D8eUVkGm1urCHKKImE+P8ELPGRe8M2zXUrTI68MXNdjdWUdUwg8r004W17E83FOmRZcuHyR4+MTXuwN6a93ufPObeZJSGfDopALjk8PKUvFymaPX/z0Uxyjheu4jIYjdveeAJqt3ipOJLn5nYsEbZ/RcYjWOZPJEa3OCiz5nmpdx/YVpUAVJgYmlaq/rJ6SKGliSRMlJGE8Z7EYYtpr5JnG2uzX0V2ZZj4as9YNCPwWtqlYpAlhtMCxFJaQvC4Hdzga8/P/+wn9Xo8PP/ohp7OILz9/TMvzePbgOcG/GeDYCcI0ORyOGc9H2DLG7be5/c5dWl0Lnce4lsl7b9/Cdj2ePN+l3Q746Ie/R5VlOJ7Efk0jgKkKlGEgbUizBJWAa5tcvLDNO2/fYp4k6Fwj1auowrQeZ1IKslTXg6soEcIgSVMm0qJISsoK3rx7mb968oij4YxfPfx66To8copC19FvacJqz6bTU3RdiVAKq7+Nt9bj4bOfklWCa70OvmdyOpvR8z02tjqUYsxf3v8Jol/QDVq0LBPldzmVPeJEw9EC3I2l66iokNKkqor6ClwKlDLRhUbnGl0WGMKgoqpnhGtBp+2wd3iAknVQ/N/9/Jf8/Jd/T5JmxGlMmuQUpebHP/4P9DqSH3zU4eDgt/B2iu+1iYqctNQUcYoELNvEcx2KvJ67YTl11pyUEsu26xdXlESLlCzTCGlQoCmKFCEEgWvS99skSQ5CgpCk+WtOc5QKlWvy4RHx+Bi/v8rO9eskmUOcTsiG++S6RCch0XQEwsCSGmkUZEcvSE736uFFVb3TqPOhBIZS2KbF4dOHWJ0OXrD8gWJZ5kyGp6RxxM1bb/Hw4UPyLOPG9evsvXxJEPgoJZHS4NHjxwB87933ARgNhzgtH9dr0Qo6lLkmTRKyrMBr+6S6RNk2vV6PN3YuLl1HoTVaV3h+j1QICiNjPJ4zmk4JwwVpfkDLdrl2+TrexkUOd5/hmIJ+t4sfeFTlDkkckeqMdb/g4PCQzorP7ZtvkmUa3+9y48Ytpq+593r12g2SeEJn1WFzK6C34SOwOXy+yywcsb61SUVYt2M5XYgiLKdg44LHxvZl7ty9Trflcnp0yFfxPsJysFXAJ3//DUf7h3x47yMcy2V/ssfqkpPmxy9nyKBFoARBUJGkBkVVII2CliMRdkWRF0hl0G4F4Dgkx0eEJ4d46Q6qFVBkKcUs5Hc/+IBwMePF3kvalomjvPqwEhWlXt6dMptGHB3sQ1Hy6S8fcv3OW2xfuES8CDndP+Tpk+coQ9ebh3FE4NiUx/sc7j+nCKcMuh4VLp7n0ApaXLr1Pv/ux/+JTuAzHp4wTitG02N+dv8xP/r40j++jqSiKAvG0TH+bIhVKe5cfxej0+bB10+ZuB7VWS5rmqVIodjaXEdKMGSFkpIsyurWV8cmnE8xlIFl2bx37waBe4H/8bf/k9F0uPT9yBdzdBpDVWFaiscPvySbnLLdDXg2PeLJ/4nqQ1B5Qhyn/MpQdNw1/vdf/DmeaxIuEhaF5DSMWJh/B+4KEWYd++bYGGcPJ7PXjIf4w3//B8wXEcfHY37x+deMxiHCkFhGPW6iqurQdH1260cXBcfDKfMoRipJrguGk5CyLLGVQV4IPnz/It99+wLJ4jGV0WORuDjV8pbLV37Dh30EGDZZkTCK55TCxJQWSVyQJbouiFWdu+e6FmlpoDyXUoMuYmJdj1atKigxzxJ2AA1CufWhISER5vK+6OlsjGGbFMqi0hk6TTl4+pT87I1P44SyKKhKDWcdFdHokCef3yfnrGZLC6kUrXaAsh0sy8KybaQ0sR0PwzRfe2hgMBhwWOQcHx7Q7/dxXZeiKNhc36Dj+4wnE9IkwbIttra2MAyD/f19dnZ2WF1bw/FaxGnK8fExlS7oBAFpmrK2toayLKIkxml5HBwt34lPZiFKmVhVhWXbKCXp93t4nkuea+ZhWD90VAYYYJkKR9mIUpDn1dkgJwPX6aDnIb2gj6ks1lZWmE6nDDY3CHorKHv5oKUizrANWO26iCplEeVsrK4RR+DZBobl4RhQYVMWksHOG/zBjxxWN1YZTof4fZeXh19xfPCM7vYqeRKfZVlmuJ7Fgwef4zoednt5d8rhN2PUSgEtxfF6iOm06EoPQ0gWaYK0LZQwoKqDDkrDpHIFti44fPYUb30TxzYp8wyMEksaKNMiS2PyIqOqDFDytVMM5/MIneWkUUy3ZyKExWg0IV5MOTk5JpxM6XVdDvf2+fST+3iWSRaecrK/ixRgtTzuvPUm777/Dt//nY/QuLitNpPZnEdPX5BUJhuDPlW5fCf+6MkewihBaC4N+rQtj7yq+Ouf/C0Hs0PGX1tkmSaJM5QpCachq/0DwukCIQWbg4A8i8nSAtezSdMMUXlsb25DJrl57V0++dWnLJLlRcsN+ihDYiCI8xi3u4HavopVFnTWt3kjiDDShG48RlQax3S5fOkddtZMJrNj9ocZYdWmm1kchhlp5SCiECONWVQFla5TfczXjAiu8opuu81aN+DqlYv89JPPePzikFm4oCorLEtiWiaGFGidI3TJ3vEYzzEpi4KyKuu/UybrA5f1rs+FjYDhZIwje/gdGztxEGL5g95XfqNFvO17zMIpZqtN33Ew5Nkc36LkVdD1q/BYZRpnHRoAErcrWKsMylL/w0hTQ1BSUZUgMDFE+WslyGytr7IeOGz2AtJ4XkczVRVVWb+59ZfrH651BeBWmls3buJYCmnbSNPCtEwctw2yvtQW1D3vCEGWZiwWyz+UaZrWKR62/W2qzWAwIPB9oD7NVlJSngUIjMZjRF7w9OlTfN+nlWsMpfB9H2UYJFHMw4cP2d/fZ/dgH9e12Npcr0Mwlqh3UXmd0lNI4jjBNBVlWT9E7PX6dQyWrFNVWq5HEARUVUWSpIDAdV1s20bnGgODVqtFHNVjBXZ2LtLp9mgHnaXrGM4mdAKLtJCcvpzy4Mtd7t5psznY4vDwJdIRdByH2SLE8Vy8rkd/cBPbsxGOSVaWzGYZlD5xrAmTBabVwg889l++wHD7RNGCFbF8HIKXepRHBTNjgXV1QcdP6mcQnoNAkKYJsjJQwqwPlIRzlGGRZTlhFmH2uzi2AUITJwuODo+JUoOWrMjS/Oxot3htSIbOS7Sue4/TVONYLVZW18naLrPRCVmS4Tl9ep0ed+/exW5Z9FyPL+7f58EXX/L2+/f43off49177+IFLq7pkuuK0/GE0Tzl5Pglw9M92q3u0nXEeYEhS0xLkImSuCr49MFnJFFCd8ckWO0gpYHONZZtQVUxm05xO5I8L5CuiddvMx6FzNOYTGfMhiFPn+whteK92/eYTqYk+WtOFlcGoqozOpXlUBmKyWJKaUpMp8PJLMKuJGHuIZXANExO53EdpZe6aNlB4sBZW3Ch07r2CIkSgtJSyLPP/dJ16ByoKEtFy7H4F7/zXd6bhOweHLK7d8Te4ZBFnCCVhZQKpeqQmJSSRZyQpAWmKbh1Y4N4nrCz0aPt+uQ5RFlKmtVdeZ32rzc7RVSvS9xuNBqNxm+tX+/xZ6PRaDR+KzVFvNFoNM6xpog3Go3GOdYU8Uaj0TjHmiLeaDQa51hTxBuNRuMca4p4o9FonGNNEW80Go1zrCnijUajcY41RbzRaDTOsaaINxqNxjnWFPFGo9E4x5oi3mg0GudYU8QbjUbjHGuKeKPRaJxjTRFvNBqNc6wp4o1Go3GONUW80Wg0zrGmiDcajcY51hTxRqPROMeaIt5oNBrnWFPEG41G4xxrinij0WicY/8fgSaA/+4nn4IAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 70 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize some examples from the dataset.\n",
    "# We show a few examples of training images from each class.\n",
    "classes = ['plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']\n",
    "num_classes = len(classes)\n",
    "samples_per_class = 7\n",
    "for y, cls in enumerate(classes):\n",
    "    idxs = np.flatnonzero(y_train == y)\n",
    "    idxs = np.random.choice(idxs, samples_per_class, replace=False)\n",
    "    for i, idx in enumerate(idxs):\n",
    "        plt_idx = i * num_classes + y + 1\n",
    "        plt.subplot(samples_per_class, num_classes, plt_idx)\n",
    "        plt.imshow(X_train[idx].astype('uint8'))\n",
    "        plt.axis('off')\n",
    "        if i == 0:\n",
    "            plt.title(cls)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train data shape:  (49000, 32, 32, 3)\n",
      "Train labels shape:  (49000,)\n",
      "Validation data shape:  (1000, 32, 32, 3)\n",
      "Validation labels shape:  (1000,)\n",
      "Test data shape:  (1000, 32, 32, 3)\n",
      "Test labels shape:  (1000,)\n"
     ]
    }
   ],
   "source": [
    "# Split the data into train, val, and test sets. In addition we will\n",
    "# create a small development set as a subset of the training data;\n",
    "# we can use this for development so our code runs faster.\n",
    "num_training = 49000\n",
    "num_validation = 1000\n",
    "num_test = 1000\n",
    "num_dev = 500\n",
    "\n",
    "# Our validation set will be num_validation points from the original\n",
    "# training set. Size = 1000\n",
    "mask = range(num_training, num_training + num_validation)\n",
    "X_val = X_train[mask]\n",
    "y_val = y_train[mask]\n",
    "\n",
    "# Our training set will be the first num_train points from the original\n",
    "# training set.  Size = 49000\n",
    "mask = range(num_training)\n",
    "X_train = X_train[mask]\n",
    "y_train = y_train[mask]\n",
    "\n",
    "# We will also make a development set, which is a small subset of\n",
    "# the training set.  Size = 500\n",
    "mask = np.random.choice(num_training, num_dev, replace=False)\n",
    "X_dev = X_train[mask]\n",
    "y_dev = y_train[mask]\n",
    "\n",
    "# We use the first num_test points of the original test set as our\n",
    "# test set.   Size = 1000\n",
    "mask = range(num_test)\n",
    "X_test = X_test[mask]\n",
    "y_test = y_test[mask]\n",
    "\n",
    "print('Train data shape: ', X_train.shape)\n",
    "print('Train labels shape: ', y_train.shape)\n",
    "print('Validation data shape: ', X_val.shape)\n",
    "print('Validation labels shape: ', y_val.shape)\n",
    "print('Test data shape: ', X_test.shape)\n",
    "print('Test labels shape: ', y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training data shape:  (49000, 3072)\n",
      "Validation data shape:  (1000, 3072)\n",
      "Test data shape:  (1000, 3072)\n",
      "dev data shape:  (500, 3072)\n"
     ]
    }
   ],
   "source": [
    "# Preprocessing: reshape the image data into rows\n",
    "X_train = np.reshape(X_train, (X_train.shape[0], -1))\n",
    "X_val = np.reshape(X_val, (X_val.shape[0], -1))\n",
    "X_test = np.reshape(X_test, (X_test.shape[0], -1))\n",
    "X_dev = np.reshape(X_dev, (X_dev.shape[0], -1))\n",
    "\n",
    "# As a sanity check, print out the shapes of the data\n",
    "print('Training data shape: ', X_train.shape)\n",
    "print('Validation data shape: ', X_val.shape)\n",
    "print('Test data shape: ', X_test.shape)\n",
    "print('dev data shape: ', X_dev.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[130.64189796 135.98173469 132.47391837 130.05569388 135.34804082\n",
      " 131.75402041 130.96055102 136.14328571 132.47636735 131.48467347]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Preprocessing: subtract the mean image\n",
    "# first: compute the image mean based on the training data\n",
    "mean_image = np.mean(X_train, axis=0)\n",
    "print(mean_image[:10]) # print a few of the elements\n",
    "plt.figure(figsize=(4,4))\n",
    "plt.imshow(mean_image.reshape((32,32,3)).astype('uint8')) # visualize the mean image\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# second: subtract the mean image from train and test data\n",
    "X_train -= mean_image\n",
    "X_val -= mean_image\n",
    "X_test -= mean_image\n",
    "X_dev -= mean_image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(49000, 3073) (1000, 3073) (1000, 3073) (500, 3073)\n"
     ]
    }
   ],
   "source": [
    "# third: append the bias dimension of ones (i.e. bias trick) so that our SVM\n",
    "# only has to worry about optimizing a single weight matrix W.\n",
    "X_train = np.hstack([X_train, np.ones( (X_train.shape[0], 1) )])  # 竖着的一列\"1\" 放在最右\n",
    "X_val = np.hstack([X_val, np.ones((X_val.shape[0], 1))])\n",
    "X_test = np.hstack([X_test, np.ones((X_test.shape[0], 1))])\n",
    "X_dev = np.hstack([X_dev, np.ones((X_dev.shape[0], 1))])\n",
    "\n",
    "print(X_train.shape, X_val.shape, X_test.shape, X_dev.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## SVM Classifier\n",
    "\n",
    "Your code for this section will all be written inside **cs231n/classifiers/linear_svm.py**. \n",
    "\n",
    "As you can see, we have prefilled the function `compute_loss_naive` which uses for loops to evaluate the multiclass SVM loss function. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting future\n",
      "Installing collected packages: future\n",
      "Successfully installed future-0.17.1\n"
     ]
    }
   ],
   "source": [
    "!pip install future"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loss: 9.144115\n"
     ]
    }
   ],
   "source": [
    "# Evaluate the naive implementation of the loss we provided for you:\n",
    "from cs231n.classifiers.linear_svm import svm_loss_naive\n",
    "import time\n",
    "\n",
    "# generate a random SVM weight matrix of small numbers\n",
    "W = np.random.randn(3073, 10) * 0.0001 \n",
    "\n",
    "loss, grad = svm_loss_naive(W, X_dev, y_dev, 0.000005)\n",
    "print('loss: %f' % (loss, ))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `grad` returned from the function above is right now all zero. Derive and implement the gradient for the SVM cost function and implement it inline inside the function `svm_loss_naive`. You will find it helpful to interleave your new code inside the existing function.\n",
    "\n",
    "To check that you have correctly implemented the gradient correctly, you can numerically estimate the gradient of the loss function and compare the numeric estimate to the gradient that you computed. We have provided code that does this for you:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "numerical: -2.634318 analytic: -2.634318, relative error: 5.574467e-11\n",
      "numerical: 9.116060 analytic: 9.116060, relative error: 5.665257e-11\n",
      "numerical: 9.153563 analytic: 9.153563, relative error: 3.541783e-11\n",
      "numerical: -2.655832 analytic: -2.655832, relative error: 9.177938e-11\n",
      "numerical: 7.216484 analytic: 7.216484, relative error: 2.972460e-11\n",
      "numerical: -33.887867 analytic: -33.887867, relative error: 5.464751e-12\n",
      "numerical: -2.016431 analytic: -2.016431, relative error: 2.927017e-10\n",
      "numerical: -9.284699 analytic: -9.284699, relative error: 9.985420e-12\n",
      "numerical: -7.743483 analytic: -7.743483, relative error: 4.076694e-11\n",
      "numerical: 17.640595 analytic: 17.640595, relative error: 1.069826e-11\n",
      "numerical: -2.965530 analytic: -2.965530, relative error: 2.527007e-11\n",
      "numerical: 34.936121 analytic: 34.936121, relative error: 3.231762e-13\n",
      "numerical: 38.196983 analytic: 38.196983, relative error: 5.214624e-12\n",
      "numerical: 15.734744 analytic: 15.734744, relative error: 1.999848e-11\n",
      "numerical: -6.318932 analytic: -6.318932, relative error: 7.684534e-12\n",
      "numerical: 9.025904 analytic: 9.025904, relative error: 6.587114e-12\n",
      "numerical: -81.624069 analytic: -81.624069, relative error: 4.769417e-12\n",
      "numerical: 18.021111 analytic: 18.021111, relative error: 1.221293e-11\n",
      "numerical: -4.807011 analytic: -4.807011, relative error: 5.593654e-11\n",
      "numerical: -37.485927 analytic: -37.485927, relative error: 3.176939e-12\n"
     ]
    }
   ],
   "source": [
    "# Once you've implemented the gradient, recompute it with the code below\n",
    "# and gradient check it with the function we provided for you\n",
    "\n",
    "# Compute the loss and its gradient at W.\n",
    "loss, grad = svm_loss_naive(W, X_dev, y_dev, 0.0)\n",
    "\n",
    "# Numerically compute the gradient along several randomly chosen dimensions, and\n",
    "# compare them with your analytically computed gradient. The numbers should match\n",
    "# almost exactly along all dimensions.\n",
    "from cs231n.gradient_check import grad_check_sparse\n",
    "f = lambda w: svm_loss_naive(w, X_dev, y_dev, 0.0)[0]\n",
    "grad_numerical = grad_check_sparse(f, W, grad)\n",
    "\n",
    "# do the gradient check once again with regularization turned on\n",
    "# you didn't forget the regularization gradient did you?\n",
    "loss, grad = svm_loss_naive(W, X_dev, y_dev, 5e1)\n",
    "f = lambda w: svm_loss_naive(w, X_dev, y_dev, 5e1)[0]\n",
    "grad_numerical = grad_check_sparse(f, W, grad)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Inline Question 1:\n",
    "It is possible that once in a while a dimension in the gradcheck will not match exactly. What could such a discrepancy be caused by? Is it a reason for concern? What is a simple example in one dimension where a gradient check could fail? *Hint: the SVM loss function is not strictly speaking differentiable*\n",
    "\n",
    "**Your Answer:** *fill this in.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Naive loss: 9.144115e+00 computed in 0.791033s\n",
      "Vectorized loss: 9.144115e+00 computed in 0.101280s\n",
      "difference: 0.000000\n"
     ]
    }
   ],
   "source": [
    "# Next implement the function svm_loss_vectorized; for now only compute the loss;\n",
    "# we will implement the gradient in a moment.\n",
    "tic = time.time()\n",
    "loss_naive, grad_naive = svm_loss_naive(W, X_dev, y_dev, 0.000005)\n",
    "toc = time.time()\n",
    "print('Naive loss: %e computed in %fs' % (loss_naive, toc - tic))\n",
    "\n",
    "from cs231n.classifiers.linear_svm import svm_loss_vectorized\n",
    "tic = time.time()\n",
    "loss_vectorized, _ = svm_loss_vectorized(W, X_dev, y_dev, 0.000005)\n",
    "toc = time.time()\n",
    "print('Vectorized loss: %e computed in %fs' % (loss_vectorized, toc - tic))\n",
    "\n",
    "# The losses should match but your vectorized implementation should be much faster.\n",
    "print('difference: %f' % (loss_naive - loss_vectorized))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Naive loss and gradient: computed in 0.892174s\n",
      "Vectorized loss and gradient: computed in 0.101717s\n",
      "difference: 0.000000\n"
     ]
    }
   ],
   "source": [
    "# Complete the implementation of svm_loss_vectorized, and compute the gradient\n",
    "# of the loss function in a vectorized way.\n",
    "\n",
    "# The naive implementation and the vectorized implementation should match, but\n",
    "# the vectorized version should still be much faster.\n",
    "tic = time.time()\n",
    "_, grad_naive = svm_loss_naive(W, X_dev, y_dev, 0.000005)\n",
    "toc = time.time()\n",
    "print('Naive loss and gradient: computed in %fs' % (toc - tic))\n",
    "\n",
    "tic = time.time()\n",
    "_, grad_vectorized = svm_loss_vectorized(W, X_dev, y_dev, 0.000005)\n",
    "toc = time.time()\n",
    "print('Vectorized loss and gradient: computed in %fs' % (toc - tic))\n",
    "\n",
    "# The loss is a single number, so it is easy to compare the values computed\n",
    "# by the two implementations. The gradient on the other hand is a matrix, so\n",
    "# we use the Frobenius norm to compare them.\n",
    "difference = np.linalg.norm(grad_naive - grad_vectorized, ord='fro')\n",
    "print('difference: %f' % difference)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Stochastic Gradient Descent\n",
    "\n",
    "We now have vectorized and efficient expressions for the loss, the gradient and our gradient matches the numerical gradient. We are therefore ready to do SGD to minimize the loss."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iteration 0 / 1500: loss 778.822939\n",
      "iteration 100 / 1500: loss 283.095431\n",
      "iteration 200 / 1500: loss 107.081289\n",
      "iteration 300 / 1500: loss 41.518431\n",
      "iteration 400 / 1500: loss 18.485510\n",
      "iteration 500 / 1500: loss 9.883193\n",
      "iteration 600 / 1500: loss 6.659906\n",
      "iteration 700 / 1500: loss 6.393922\n",
      "iteration 800 / 1500: loss 5.441120\n",
      "iteration 900 / 1500: loss 4.985440\n",
      "iteration 1000 / 1500: loss 5.533671\n",
      "iteration 1100 / 1500: loss 5.768921\n",
      "iteration 1200 / 1500: loss 5.578299\n",
      "iteration 1300 / 1500: loss 5.213661\n",
      "iteration 1400 / 1500: loss 5.357082\n",
      "That took 22.404552s\n"
     ]
    }
   ],
   "source": [
    "# In the file linear_classifier.py, implement SGD in the function\n",
    "# LinearClassifier.train() and then run it with the code below.\n",
    "from cs231n.classifiers import LinearSVM\n",
    "svm = LinearSVM()\n",
    "tic = time.time()\n",
    "loss_hist = svm.train(X_train, y_train, learning_rate=1e-7, reg=2.5e4,\n",
    "                      num_iters=1500, verbose=True)\n",
    "toc = time.time()\n",
    "print('That took %fs' % (toc - tic))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# A useful debugging strategy is to plot the loss as a function of\n",
    "# iteration number:\n",
    "plt.plot(loss_hist)\n",
    "plt.xlabel('Iteration number')\n",
    "plt.ylabel('Loss value')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "training accuracy: 0.361796\n",
      "validation accuracy: 0.373000\n"
     ]
    }
   ],
   "source": [
    "# Write the LinearSVM.predict function and evaluate the performance on both the\n",
    "# training and validation set\n",
    "y_train_pred = svm.predict(X_train)\n",
    "print('training accuracy: %f' % (np.mean(y_train == y_train_pred), ))\n",
    "y_val_pred = svm.predict(X_val)\n",
    "print('validation accuracy: %f' % (np.mean(y_val == y_val_pred), ))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "lr 1.000000e-07 reg 1.000000e+02 train accuracy: 0.300061 val accuracy: 0.299000\n",
      "lr 1.000000e-07 reg 5.000000e+02 train accuracy: 0.302735 val accuracy: 0.317000\n",
      "lr 1.000000e-07 reg 1.000000e+03 train accuracy: 0.313388 val accuracy: 0.316000\n",
      "lr 1.000000e-07 reg 5.000000e+03 train accuracy: 0.353939 val accuracy: 0.337000\n",
      "lr 1.000000e-07 reg 5.000000e+04 train accuracy: 0.361367 val accuracy: 0.369000\n",
      "lr 1.000000e-07 reg 5.000000e+05 train accuracy: 0.281388 val accuracy: 0.297000\n",
      "lr 2.000000e-07 reg 1.000000e+02 train accuracy: 0.316714 val accuracy: 0.307000\n",
      "lr 2.000000e-07 reg 5.000000e+02 train accuracy: 0.329143 val accuracy: 0.321000\n",
      "lr 2.000000e-07 reg 1.000000e+03 train accuracy: 0.346551 val accuracy: 0.358000\n",
      "lr 2.000000e-07 reg 5.000000e+03 train accuracy: 0.379633 val accuracy: 0.378000\n",
      "lr 2.000000e-07 reg 5.000000e+04 train accuracy: 0.348184 val accuracy: 0.346000\n",
      "lr 2.000000e-07 reg 5.000000e+05 train accuracy: 0.286490 val accuracy: 0.298000\n",
      "lr 5.000000e-07 reg 1.000000e+02 train accuracy: 0.352327 val accuracy: 0.340000\n",
      "lr 5.000000e-07 reg 5.000000e+02 train accuracy: 0.364204 val accuracy: 0.356000\n",
      "lr 5.000000e-07 reg 1.000000e+03 train accuracy: 0.372837 val accuracy: 0.377000\n",
      "lr 5.000000e-07 reg 5.000000e+03 train accuracy: 0.340653 val accuracy: 0.348000\n",
      "lr 5.000000e-07 reg 5.000000e+04 train accuracy: 0.328878 val accuracy: 0.334000\n",
      "lr 5.000000e-07 reg 5.000000e+05 train accuracy: 0.271837 val accuracy: 0.296000\n",
      "best validation accuracy achieved during cross-validation: 0.378000\n"
     ]
    }
   ],
   "source": [
    "# Use the validation set to tune hyperparameters (regularization strength and\n",
    "# learning rate). You should experiment with different ranges for the learning\n",
    "# rates and regularization strengths; if you are careful you should be able to\n",
    "# get a classification accuracy of about 0.4 on the validation set.\n",
    "learning_rates = [1e-7, 2e-7, 5e-7]\n",
    "regularization_strengths = [1e2, 5e2, 1e3, 5e3, 5e4, 5e5]\n",
    "\n",
    "# results is dictionary mapping tuples of the form\n",
    "# (learning_rate, regularization_strength) to tuples of the form\n",
    "# (training_accuracy, validation_accuracy). The accuracy is simply the fraction\n",
    "# of data points that are correctly classified.\n",
    "results = {}\n",
    "best_val = -1   # The highest validation accuracy that we have seen so far.\n",
    "best_svm = None # The LinearSVM object that achieved the highest validation rate.\n",
    "\n",
    "################################################################################\n",
    "# Write code that chooses the best hyperparameters by tuning on the validation #\n",
    "# set. For each combination of hyperparameters, train a linear SVM on the      #\n",
    "# training set, compute its accuracy on the training and validation sets, and  #\n",
    "# store these numbers in the results dictionary. In addition, store the best   #\n",
    "# validation accuracy in best_val and the LinearSVM object that achieves this  #\n",
    "# accuracy in best_svm.                                                        #\n",
    "#                                                                              #\n",
    "# Hint: You should use a small value for num_iters as you develop your         #\n",
    "# validation code so that the SVMs don't take much time to train; once you are #\n",
    "# confident that your validation code works, you should rerun the validation   #\n",
    "# code with a larger value for num_iters.                                      #\n",
    "################################################################################\n",
    "for learning_rate in learning_rates:\n",
    "  for reg in regularization_strengths:\n",
    "    svm = LinearSVM()\n",
    "    svm.train(X_train, y_train, learning_rate=learning_rate, reg=reg,\n",
    "              num_iters=1200, verbose=False)\n",
    "    y_train_pred = svm.predict(X_train)\n",
    "    y_train_acc = np.mean(y_train == y_train_pred)\n",
    "    y_val_pred = svm.predict(X_val)\n",
    "    y_val_acc = np.mean(y_val == y_val_pred)\n",
    "    results[(learning_rate, reg)] = (y_train_acc, y_val_acc)\n",
    "\n",
    "    if y_val_acc > best_val:\n",
    "      best_val = y_val_acc\n",
    "      best_svm = svm\n",
    "################################################################################\n",
    "#                              END OF YOUR CODE                                #\n",
    "################################################################################\n",
    "    \n",
    "# Print out results.\n",
    "for lr, reg in sorted(results):\n",
    "    train_accuracy, val_accuracy = results[(lr, reg)]\n",
    "    print('lr %e reg %e train accuracy: %f val accuracy: %f' % (\n",
    "                lr, reg, train_accuracy, val_accuracy))\n",
    "    \n",
    "print('best validation accuracy achieved during cross-validation: %f' % best_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize the cross-validation results\n",
    "import math\n",
    "x_scatter = [math.log10(x[0]) for x in results]\n",
    "y_scatter = [math.log10(x[1]) for x in results]\n",
    "\n",
    "# plot training accuracy\n",
    "marker_size = 100\n",
    "colors = [results[x][0] for x in results]\n",
    "plt.subplot(2, 1, 1)\n",
    "plt.scatter(x_scatter, y_scatter, marker_size, c=colors)\n",
    "plt.colorbar()\n",
    "plt.xlabel('log learning rate')\n",
    "plt.ylabel('log regularization strength')\n",
    "plt.title('CIFAR-10 training accuracy')\n",
    "\n",
    "# plot validation accuracy\n",
    "colors = [results[x][1] for x in results] # default size of markers is 20\n",
    "plt.subplot(2, 1, 2)\n",
    "plt.scatter(x_scatter, y_scatter, marker_size, c=colors)\n",
    "plt.colorbar()\n",
    "plt.xlabel('log learning rate')\n",
    "plt.ylabel('log regularization strength')\n",
    "plt.title('CIFAR-10 validation accuracy')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "linear SVM on raw pixels final test set accuracy: 0.363000\n"
     ]
    }
   ],
   "source": [
    "# Evaluate the best svm on test set\n",
    "y_test_pred = best_svm.predict(X_test)\n",
    "test_accuracy = np.mean(y_test == y_test_pred)\n",
    "print('linear SVM on raw pixels final test set accuracy: %f' % test_accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 10 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize the learned weights for each class.\n",
    "# Depending on your choice of learning rate and regularization strength, these may\n",
    "# or may not be nice to look at.\n",
    "w = best_svm.W[:-1,:] # strip out the bias\n",
    "w = w.reshape(32, 32, 3, 10)\n",
    "w_min, w_max = np.min(w), np.max(w)\n",
    "classes = ['plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']\n",
    "for i in range(10):\n",
    "    plt.subplot(2, 5, i + 1)\n",
    "      \n",
    "    # Rescale the weights to be between 0 and 255\n",
    "    wimg = 255.0 * (w[:, :, :, i].squeeze() - w_min) / (w_max - w_min)\n",
    "    plt.imshow(wimg.astype('uint8'))\n",
    "    plt.axis('off')\n",
    "    plt.title(classes[i])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Inline question 2:\n",
    "Describe what your visualized SVM weights look like, and offer a brief explanation for why they look they way that they do.\n",
    "\n",
    "**Your answer:** *fill this in*"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
